![\"Natural](\"images/chapter7_neuron.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Rosenblatt further developed the artificial neuron to give it the ability to learn. Even more importantly, he worked on building the first device that actually used these principles: The Mark I Perceptron. Rosenblatt wrote about this work: \"we are about to witness the birth of such a machine – a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control\". The perceptron was built, and was able to successfully recognize simple shapes.\n",
+ "\n",
+ "An MIT professor named Marvin Minsky (who was a grade behind Rosenblatt the same high school!) along with Seymour Papert wrote a book, called \"Perceptrons\", about Rosenblatt's invention. They showed that a single layer of these devices was unable to learn some simple, critical mathematical functions (such as XOR). In the same book, they also showed that using multiple layers of the devices would allow these limitations to be addressed. Unfortunately, only the first of these insights was widely recognized, as a result of which the global academic community nearly entirely gave up on neural networks for the next two decades."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Perhaps the most pivotal work in neural networks in the last 50 years is the multi-volume *Parallel Distributed Processing*, released in 1986 by MIT Press. Chapter 1 lays out a similar hope to that shown by Rosenblatt:\n",
+ "\n",
+ "> : _…people are smarter than today s computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at. …we will introduce a computational framework for modeling cognitive processes that seems… closer than other frameworks to the style of computation as it might be done by the brain._ (Parallel distributed processing, chapter 1)\n",
+ "\n",
+ "It defined \"Parallel Distributed Processing\" as requiring:\n",
+ "\n",
+ "1. A set of *processing units*\n",
+ "1. A *state of activation*\n",
+ "1. An *output function* for each unit \n",
+ "1. A *pattern of connectivity* among units \n",
+ "1. A *propagation rule* for propagating patterns of activities through the network of connectivities \n",
+ "1. An *activation rule* for combining the inputs impinging on a unit with the current state of that unit to produce a new level of activation for the unit\n",
+ "1. A *learning rule* whereby patterns of connectivity are modified by experience \n",
+ "1. An *environment* within which the system must operate\n",
+ "\n",
+ "We will learn in this book about how modern neural networks handle each of these requirement. In the 1980's most models were built with a second layer of neurons, thus avoiding the problem that had been identified by Minsky (this was their \"pattern of connectivity among units\", to use the framework above). And indeed, neural networks were widely used during the 80s and 90s for real, practical projects. However, again a misunderstanding of the theoretical issues held back the field. In theory, adding just one extra layer of neurons was enough to allow any mathematical model to be approximated with these neural networks, but in practice such networks were often too big and slow to be useful.\n",
+ "\n",
+ "Although there were researchers 30 years ago showing that to get good performance in practice you need to use even more layers of neurons, it is only in the last decade that this has been more widely appreciated. Thanks to this understanding, along with the improved ability to use these in practice thanks to improvements in computer hardware, increases in data availability, and algorithmic tweaks that allow neural networks to be trained faster and more easily, neural networks are now finally living out their potential. We now have what Rosenblatt had promised: \"a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control\". And you will learn how to build them in this book."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What you will learn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After reading this book, you will know:\n",
+ "\n",
+ "- How to train models that achieve state of the art results in:\n",
+ " - Computer vision: Image classification (e.g. classify pet photos by breed), and image localization and detection (e.g. find where the animals in an image are)\n",
+ " - Natural Language Processing (NLP): Document classification (e.g. movie review sentiment analysis), and language modelling\n",
+ " - Tabular data (e.g. sales prediction) with categorical data, continuous data, and mixed data, including time series\n",
+ " - Collaborative filtering (e.g. movie recommendation)\n",
+ "- How to turn your models into web applications\n",
+ "- Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models\n",
+ "- The latest deep learning techniques which really matter in practice\n",
+ "- How to read a deep learning research paper\n",
+ "- How to implement deep learning algorithms from scratch\n",
+ "- How to think about ethical implications of your work, to help ensure that you're making the world a better place, and that your work isn't misused for harm\n",
+ "\n",
+ "See the table of contents for a complete list; but to give you a taste, here's some of the techniques covered (don't worry if none of these words mean anything to you yet – you'll learn them all soon): Affine functions and non-linearities; Parameters and activations; Random init and transfer learning; SGD, Momentum, Adam and more optimizers; Convolutions; Batch normalization; Dropout; Data augmentation; Weight decay; Resnet and Densenet architectures; Image classification and regression; Embeddings; Recurrent neural networks (RNNs); Transformers; Segmentation; U-net; Generative Adversarial Networks (GANs), and much more."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: If you look at the end of each chapter, you'll find a questionnaire. That's a great place also to see what we cover in each chapter, since (we hope!) by the end of each chapter you'll be able to answer all the questions there. In fact, one of our reviewers (thanks Fred!) said that he likes to read the questionnaire *first*, before reading the chapter, so that way he knows what to look for."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Who we are"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since we are going to be spending a lot of time together, let's get to know each other a bit… We are Sylvain and Jeremy, your guides on this journey. We hope that you will find us well suited for this position.\n",
+ "\n",
+ "Jeremy has been using and teaching machine learning for around 30 years. He started using neural networks 25 years ago. During this time he has led many companies and projects which have machine learning at their core, including founding the first company to focus on deep learning and medicine, Enlitic, and taking on the role of Pres and chief scientist of the world's largest machine learning community, Kaggle. He is the co-founder, along with Dr Rachel Thomas, of fast.ai, the organisation which built the course that this book is based on.\n",
+ "\n",
+ "From time to time you will hear directly from us, in sidebars like this one from Jeremy:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> J: Hi everybody, I'm Jeremy! You might be interested to know that I do not have any formal technical education. I completed a Bachelor of Arts, with a major in philosophy, and didn't do very well in my university grades. I was much more interested in doing real projects, rather than theoretical studies, so I worked full-time at a management consulting firm called McKinsey and Company throughout my degree. If you're somebody who would rather get their hands dirty building stuff rather than spend years learning abstract concepts, then you will understand where I am coming from! Look out for sidebars from me to find information most suited to people with a less mathematical or formal technical background—that is, people like me…"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sylvain, on the other hand, knows a lot about formal technical education. In fact, he has written 10 maths textbooks, covering the entire advanced French maths curriculum!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> S: Unlike Jeremy, I have not spent many years coding and applying machine learning algorithms. Rather, I recently came to the machine learning world, by watching Jeremy's fast.ai course videos. So, if you are somebody who has not opened a terminal and written commands at the command line, then you will understand where I am coming from! Look out for sidebars from me to find information most suited to people with a more mathematical or formal technical background, but less real-world coding—that is, people like me…"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fast.ai course has been studied by hundreds of thousands of students, from all walks of life, from all parts of the world. Sylvain stood out as the most impressive student of the course that Jeremy had ever seen, which led to him joining fast.ai, and then becoming the co-author, along with Jeremy, of the fastai software library.\n",
+ "\n",
+ "All this means that you have the best of both worlds: the people who know more about the software than anybody, because they wrote it, an expert on maths, and an expert on coding and machine learning, but also people who understand what it feels like to be a relative outsider in maths, and a relative outsider in coding and machine learning.\n",
+ "\n",
+ "Anybody who has watched sports knows that if you have a two-person commentary team then you also need a third person to do \"special comments\". Our special commentator is Alexis Gallagher. Alexis has a very diverse background: he has been a zoology researcher, screenplay writer, an improv performer, a McKinsey consultant (like Jeremy!), a Swift coder, and a CTO."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> A: I've decided it's time for me to learn about this AI stuff! After all, I've tried pretty much everything else… But I don't really have a background in machine learning, or in Python. Still… how hard can it be? I'm going to be learning throughout this book, just like you are. Look out for my sidebars for learning tips that I found helpful on my journey, and hopefully you will find helpful too."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How to learn deep learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Harvard professor David Perkins, who wrote Making Learning Whole, has much to say about this. The basic idea is to teach the *whole game*. That means that's if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to line thread into a ball, the physics of a parabola, or the coefficient of friction of a ball on a bat.\n",
+ "\n",
+ "Paul Lockhart, a Columbia math PhD, former Brown professor, and K-12 math teacher, imagines in the influential essay A Mathematician's Lament a nightmare world where music and art are taught the way math is taught. Children would not be allowed to listen to or play music until they have spent over a decade mastering music notation and theory, spending classes transposing sheet music into a different key. In art class, students study colours and applicators, but aren't allowed to actually paint until college. Sound absurd? This is how math is taught–we require students to spend years doing rote memorization, and learning dry, disconnected *fundamentals* that we claim will pay off later, long after most of them quit the subject.\n",
+ "\n",
+ "Unfortunately, this is where many teaching resources on deep learning begin–asking learners to follow along with the definition of the Hessian and theorems for the Taylor approximation of your loss function, without ever giving examples of actual working code. We're not knocking calculus. We love calculus and have even taught it at the college level, but we don't think it's the best place to start when learning deep learning!\n",
+ "\n",
+ "In deep learning, it really helps if you have the motivation to fix your model to get it to do better. That's when you start learning the relevant theory. But you need to have the model in the first place. We teach almost everything through real examples. As we build out those examples, we go deeper and deeper, and we'll show you how to make your projects better and better. This means that you'll be gradually learning all the theoretical foundations you need, in context, in a way that you'll see why it matters and how it works.\n",
+ "\n",
+ "So, here's our commitment to you. Throughout this book, we will follow these principles:\n",
+ "\n",
+ "- Teaching the *whole game* – starting off by showing how to use a complete, working, very usable, state of the art deep learning network to solve real world problems, by using simple, expressive tools. And then gradually digging deeper and deeper into understanding how those tools are made, and how the tools that make those tools are made, and so on…\n",
+ "- Always teaching through examples: ensuring that there is a context and a purpose that you can understand intuitively, rather than starting with algebraic symbol manipulation ;\n",
+ "- Simplifying as much as possible: we've spent years building tools and teaching methods that make previously complex topics very simple ;\n",
+ "- Removing barriers: deep learning has, until now, been a very exclusive game. We're breaking it open, and ensuring that everyone can play."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The hardest part of deep learning is artisanal: how do you know if you've got enough data; whether it is in the right format; if your model is training properly; and if it's not, what should you do about it? That is why we believe in learning by doing. As with basic data science skills, with deep learning you only get better through practical experience. Trying to spend too much time on the theory can be counterproductive. The key is to just code and try to solve problems: the theory can come later, when you have context and motivation.\n",
+ "\n",
+ "There will be times when the journey will feel hard. Times where you feel stuck. Don't give up! Rewind through the book to find the last bit where you definitely weren't stuck, and then read slowly through from there to find the first thing that isn't clear. Then try some code experiments yourself, and Google around for more tutorials on whatever the issue you're stuck with is--often you'll find some different angle on the material which might help it to click. Also, it's to not understand everything on first reading. Trying to understand the material serially before proceeding can sometimes be hard. Sometimes things click into place after you got more context from parts down the road, from having a bigger picture. So if you do get stuck on a section, trying moving on anyway and make a note to come back to it later.\n",
+ "\n",
+ "Remember, you don't need any particular academic background to succeed at deep learning. Many important breakthroughs are made in research and industry by folks without a PhD, such as the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), one of the most influential papers of the last decade, with over 5000 citations, which was written by Alec Radford when he was an under-graduate. Even at Tesla, where they're trying to solve the extremely tough challenge of making a self-driving car, CEO [Elon Musk says](https://twitter.com/elonmusk/status/1224089444963311616):\n",
+ "\n",
+ "> : \"A PhD is definitely not required. All that matters is a deep understanding of AI & ability to implement NNs in a way that is actually useful (latter point is what’s truly hard). Don’t care if you even graduated high school.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Your projects and your mindset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Whether you're excited to identify if plants are diseased from pictures of their leaves, auto-generate knitting patterns, diagnose TB from x-rays, or determine when a raccoon is using your cat door, we will get you using deep learning on your own problems (via pre-trained models from others) as quickly as possible, and then will progressively drill into more details. You'll learn how to use deep learning to solve your own problems at state-of-the-art accuracy within the first 30 minutes of the next chapter! (And feel free to skip straight to there now if you're dying to get coding right away.) There is a pernicious myth out there that you need to have computing resources and datasets the size of those at Google to be able to do deep learning, and it's not true.\n",
+ "\n",
+ "So, what sort of tasks make for good test cases? You could train your model to distinguish between Picasso and Monet paintings or to pick out pictures of your daughter instead of pictures of your son. It helps to focus on your hobbies and passions–setting yourself four of five little projects rather than striving to solve a big, grand problem tends to work better when you're getting started. Since it is easy to get stuck, trying to be too ambitious too early can often backfire. Then, once you've got the basics mastered, aim to complete something you're really proud of!\n",
+ "\n",
+ "Common character traits in the people that do well at deep learning include playfulness and curiosity. The late physicist Richard Feynman is an example of someone who we'd expect to be great at deep learning: his development of an understanding of the movement of subatomic particles came from his amusement at how plates wobble when they spin in the air."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> J: Deep learning can be set to work on almost any problem. For instance, my first startup was a company called FastMail, which provided enhanced email services when it launched in 1999 (and still does to this day). In 2002 I set it up to use a primitive form of deep learning – single-layer neural networks – to help to categorise emails and stop customers from receiving spam."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The software: PyTorch, fastai, and Jupyter (and why it doesn't matter)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've completed hundreds of machine learning projects using dozens of different packages, and many different programming languages. At fast.ai, we have written courses using most of the main deep learning and machine learning packages used today. After PyTorch came out in 2017 we spent over a thousand hours testing it before deciding that we would use it for future courses, software development, and research. Since that time PyTorch has become the world's fastest-growing deep learning library and is already used for most research papers at top conferences. This is generally a leading indicator of usage in industry, because these are the papers that end up getting used in products and services commercially. We have found that PyTorch is the most flexible and expressive library for deep learning. It does not trade off speed for simplicity, but provides both.\n",
+ "\n",
+ "PyTorch works best as a low-level foundation library, providing the basic operations for higher level functionality. The fastai library is the most popular library for adding this higher-level functionality on top of PyTorch. It's also particularly well suited for the purposes of this book, because it is unique in providing a deeply layered software architecture (there's even a [peer-reviewed academic paper](https://arxiv.org/abs/2002.04688) about this layered API). In this book, as we go deeper and deeper into the foundations of deep learning, we will also go deeper and deeper into the layers of fastai. This book covers version 2 of the fastai library, which is a from-scratch rewrite providing many unique features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "However, it doesn't really matter what software you learn, because it takes only a few days to learn to switch from one library to another. What really matters is learning the deep learning foundations and techniques properly. Our focus will be on using code which as clearly as possible expresses the concepts that you need to learn. Where we are teaching high-level concepts, we will use high level fastai code. Where we are teaching low-level concepts, we will use low-level PyTorch, or even pure Python code.\n",
+ "\n",
+ "If it feels like new deep learning libraries are appearing at a rapid pace nowadays, then you need to be prepared for a much faster rate of change in the coming months and years. As more people enter the field, they will bring more skills and ideas, and try more things. You should assume that whatever specific libraries and software you learn today will be obsolete in a year or two. Just think about the number of changes of libraries and technology stacks that occur all the time in the world of web programming — and yet this is a much more mature and slow-growing area than deep learning. We strongly believe that the focus in learning needs to be on understanding the underlying techniques and how to apply them in practice, and how to quickly build expertise in new tools and techniques as they are released."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By the end of the book, you'll understand nearly all the code that's inside fastai (and much of PyTorch too), because each chapter we'll be digging a level deeper to understand exactly what's going on as we build and train our models. This means that you'll have learnt the most important best practices used in modern deep learning—not just how to use them, but how they really work and are implemented. If you want to use those approaches in another framework, you'll have the knowledge you need to develop it if needed.\n",
+ "\n",
+ "Since the most important thing for learning deep learning is writing code and experimenting, it's important that you have a great platform for experimenting with code. The most popular programming experimentation platform is called Jupyter. This is what we will be using throughout this book. We will show you how you can use Jupyter to train and experiment with models and introspect every stage of the data pre-processing and model development pipeline. Jupyter is the most popular tool for doing data science in Python, for good reason. It is powerful, flexible, and easy to use. We think you will love it!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Your first model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Getting a GPU deep learning server"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To do nearly everything in this course, you'll need access to a computer with an NVIDIA GPU (unfortunately other brands of GPU are not fully supported by the main deep learning libraries). However, we don't recommend you buy one; in fact, even if you already have one, we don't suggest you use it just yet! Setting up a computer takes time and energy, and you want all your energy to focus on deep learning right now. Therefore, we instead suggest you rent access to a computer that already has everything you need preinstalled and ready to go. Costs can be as little as US$0.25 per hour while you're using it, and some options are even free."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: (Graphic Processing Unit) GPU: Also known as a *graphics card*. A special kind of processor in your computer than can handle thousands of single tasks at the same time, especially designed for displaying 3d environments on a computer for playing games. These same basic tasks are very similar to what neural networks do, such that GPUs can run neural networks hundreds of times faster than regular CPUs. All modern computers contain a GPU, but few contain the right kind of GPU necessary for deep learning."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The best choice for GPU servers for use with this book change over time, as companies come and go, and prices change. We keep a list of our recommended options on the [book website](https://book.fast.ai/). So, go there now, and follow the instructions to get connected to a GPU deep learning server. Don't worry, it only takes about two minutes to get set up on most platforms, and many don't even require any payment, or even a credit card to get started.\n",
+ "\n",
+ "> A: My two cents: heed this advice! If you like computers you will be tempted to setup your own box. Beware! It is feasible but surprisingly involved and distracting. There is a good reason this book is not titled, _Everything you ever wanted to know about Ubuntu system administration, NVIDIA driver installation, apt-get, conda, pip, and Jupyter notebook configuration_. That would be a book of its own. Having designed and deployed our production machine learning infrastructure at work, I can testify it has its satisfactions but it is as unrelated to understanding models as maintaining an airplane is from flying one.\n",
+ "\n",
+ "Each option shown on the book website includes a tutorial; after completing the tutorial, you will end up with a screen looking like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Initial](\"images/att_00057.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You are now ready to run your first Jupyter notebook!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Jupyter Notebook: A piece of software that allows you to include formatted text, code, images, videos, and much more, all within a single interactive document. Jupyter received the highest honor for software, the ACM Software System Award, thanks to its wide use and enormous impact in many academic fields, and in industry. Jupyter Notebook is the most widely used software by data scientists for developing and interacting with deep learning models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Running your first notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The notebooks are labelled by chapter, and then by notebook number, so that they are in the same order as they are presented in this book. So, the very first notebook you will see listed, is the notebook that we need to use now. You will be using this notebook to train a model that can recognize dog and cat photos. To do this, we'll be downloading a _dataset_ of dog and cat photos, and using that to _train a model_. A _dataset_ simply refers to a bunch of data—it could be images, emails, financial indicators, sounds, or anything else. There are many datasets made freely available that are suitable for training models. Many of these datasets are created by academics to help advance research, many are made available for competitions (there are competitions where data scientists can compete to see who has the most accurate model!), and some are by-products of other processes (such as financial filings)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: There are two folders containing different versions of the notebooks. The **full** folder contains the exact notebooks used to create the book you're reading now, with all the prose and outputs. The **stripped** version has the same headings and code cells, but all outputs and prose have been removed. After reading a section of the book, we recommend working through the stripped notebooks, with the book closed, and see if you can figure out what each cell will show before you execute it. And try to recall what the code is demonstrating."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To open a notebook, just click on it. The notebook will open, and it will look something like this (note that there may be slight differences in details across different platforms; you can ignore those differences):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"An](\"images/0_jupyter.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A notebook consists of _cells_. There are two main types of cell:\n",
+ "\n",
+ "- Cells containing formatted text, images, and so forth. These use a format called _markdown_, which we will learn about soon\n",
+ "- Cells containing code, which can be executed, and outputs will appear immediately underneath (which could be plain text, tables, images, animations, sounds, or even interactive applications)\n",
+ "\n",
+ "Jupyter notebooks can be in one of two modes, edit mode, or command mode. In edit mode typing the keys on your keyboard types the letters into the cell in the usual way. However, in command mode, you will not see any flashing cursor, and the keys on your keyboard will each have a special function.\n",
+ "\n",
+ "Let's make sure that you are in command mode before continuing: press \"escape\" now on your keyboard to switch to command mode (if you are already in command mode, then this does nothing, so press it now just in case). To see a complete list of all of the functions available, press \"h\"; press \"escape\" to remove this help screen. Notice that in command mode, unlike most programs, commands do not require you to hold down \"control\", \"alt\", or similar — you simply press the required letter key.\n",
+ "\n",
+ "You can make a copy of a cell by pressing \"c\" (it needs to be selected first, indicated with an outline around the cell; if it is not already selected, click on it once). Then press \"v\" to paste a copy of it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When you click on a cell it will be selected. Click on the cell now which begins with the line \"# CLICK ME\". The first character in that line represents a comment in Python, so is ignored when executing the cell. The rest of the cell is, believe it or not, a complete system for creating and training a state-of-the-art model for recognizing cats versus dogs. So, let's train it now! To do so, just press shift-enter on your keyboard, or press the \"play\" button on the toolbar. Then, wait a few minutes while the following things happen:\n",
+ "\n",
+ "1. A dataset containing called the [Oxford-IIT Pet Dataset](http://www.robots.ox.ac.uk/~vgg/data/pets/) that contains 7,349 images of cats and dogs from 37 different breeds will be downloaded from the fast.ai datasets collection to your GPU server, and will then be extracted\n",
+ "2. A *pretrained model* will be downloaded from the Internet, which has already been trained on 1.3 million images, using a competition winning model\n",
+ "3. The pretrained model will be *fine-tuned* using the latest advances in transfer learning, to create a model that is specially customised for recognising dogs and cats\n",
+ "\n",
+ "The first two steps only need to be run once on your GPU server. If you run the cell again, it will use the dataset and model that have already been downloaded, rather than downloading them again."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.056831 | \n", + "0.033615 | \n", + "0.006766 | \n", + "00:18 | \n", + "
![\"An](\"images/att_00008.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can pass the uploaded file to the model. The notebook will tell you whether it thinks it is a dog, or a cat, and how confident it is. Make sure that it is a clear photo of a single dog or a cat, and not a line drawing, cartoon, or similar. Hopefully, you'll find that your model did a great job!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "# For the book, we can't actually click an upload button, so we fake it\n",
+ "uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ "![\"Example](\"images/att_00000.png\" "\\"Example")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Overfitting is the most important and challenging single issue** when training for all machine learning practitioners, and all algorithms. As we will see, it is very easy to create a model that does a great job at making predictions on the exact data which it has been trained on, but it is much harder to make predictions on data that it has never seen before. And of course this is the data that will actually matter in practice. For instance, if you create and hand-written digit classifier (as we will very soon!) and use it to recognise numbers written on cheques, then you are never going to see any of the numbers that the model was trained on -- every cheque will have slightly different variations of writing to deal with. We will learn many methods to avoid overfitting in this book. However, you should only use those methods after you have confirmed that overfitting is actually occuring (i.e. you have actually observed the validation accuracy getting worse during training). We often see practitioners using over-fitting avoidance techniques even when they have enough data that they didn't need to do so, ending up with a model that could be less accurate than what they could have gotten."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> important: When you train a model, you must **always** have both a training set, and a validation set, and must measure the accuracy of your model only on the validation set. If you train for too long, with not enough data, you will see the accuracy of your model start to get worse; this is called **over-fitting**. fastai defaults `valid_pct` to `0.2`, so even if you forget, fastai will create a validation set for you!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
+ "```\n",
+ "\n",
+ "The fourth line tells fastai to create a *convolutional neural network* (CNN), and selects what *architecture* to use (i.e. what kind of model to create), what data we want to train it on, and what *metric* to use. A CNN is the current state of the art approach to creating computer vision models. We'll be learning all about how they work in this book. Their structure is inspired by how the human vision system works.\n",
+ "\n",
+ "There are many different *architectures* in fastai, which we will be learning about in this book, as well as discussing how to create your own. Most of the time, however, picking an architecture isn't a very important part of the deep learning process. It's something that academics love to talk about, but in practice it is unlikely to be something you need to spend much time on. There are some standard architectures that work most of the time, and in this case we're using one called _ResNet_ that will be learning a lot about during the book, and is both fast and accurate for many datasets and problems. The \"34\" in `resnet34` refers to the number of layers in this variant of the architecture (other options are \"18\", \"50\", \"101\", and \"152\"). Models using architectures with more layers take longer to train, and are more prone to overfitting (i.e. you can't train them for as many epochs before the accuracy on the validation set starts getting worse). On the other hand, when using more data, they can be quite a bit more accurate.\n",
+ "\n",
+ "A *metric* is a function that is called to measure how good the model is, using the validation set, and will be printed at the end of each *epoch*. In this case, we're using `error_rate`, which is a function provided by fastai which does just what it says: tells you what percentage of images in the validation set are being classified incorrectly. Another common metric for classification is `accuracy` (which is just `1.0 - error_rate`). fastai provides many more, which will be discussed throughout this book."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`cnn_learner` also has a parameter `pretrained`, which defaults to `True` (so it's used in this case), which sets the weights in your model to values that have already been trained by experts to recognize a thousand different categories across 1.3 million photos (using the famous *ImageNet* dataset). A model that has weights that have already been trained on some other dataset is called a *pretrained model*. You should nearly always use a pretrained model, because it means that your model, before you've even shown it any of your data, is already very capable. And, as you'll see, in a deep learning model many of these capabilities are things you'll need, almost regardless of the details of your project (such as edge, gradient, and color detection).\n",
+ "\n",
+ "When using a pretrained model, `cnn_learner` will remove the last layer, since that is always specifically customized to the original training task (i.e. ImageNet dataset classification), and replace it with one or more new layers with randomized weights, of an appropriate size for the dataset you are working with. This last part of the model is known as the `head`.\n",
+ "\n",
+ "Using pretrained models is the *most* important method we have to allow us to train more accurate models, more quickly, with less data, and less time and money. You might think that would mean that using pretrained models would be the most studied area in academic deep learning... but you'd be very very wrong! The importance of pretrained models is generally not recognized or discussed in most courses, books, or software library features, and is rarely considered in academic papers. As we write this at the start of 2020, things are just starting to change, but it's likely to take a while. So be careful: most people you speak to will probably greatly underestimate what you can do in deep learning with few resources, because they probably won't deeply understand how to use pretrained models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "learn.fine_tune(1)\n",
+ "```\n",
+ "\n",
+ "The fifth line tells fastai how to *fit* the model. As we've discussed, the architecture only describes a *template* for a mathematical function; but it doesn't actually do anything until we provide values for the millions of parameters it contains.\n",
+ "\n",
+ "This is the key to deep learning — how to fit the parameters of a model to get it to solve your problem. In order to fit a model, we have to provide at least one piece of information: how many times to look at each image (known as number of *epochs*). The number of epochs you select will largely depend on how much time you have available, and how long you find it takes in practice to fit your model. If you select a number that is too small, you can always train for more epochs later.\n",
+ "\n",
+ "But why is the method called `fine_tune`, and not `fit`? fastai actually *does* have a method called `fit`, which does indeed fit a model (i.e. look at images in the training set multiple times, each time updating the *parameters* to make the predictions closer and closer to the *target labels*). But in this case, we've started with a pretrained model, and we don't want to through away all those capabilities that it already has. As we'll learn in this book, there are some important tricks to adapt a pretrained model for a new dataset -- a process called *fine-tuning*. When you use the `fine_tune` method, fastai will use these tricks for you. There are a few parameters you can set (which we'll discuss later), but in the default form shown here, it does two steps:\n",
+ "\n",
+ "1. Use one *epoch* to fit just those parts of the model necessary to get the new random *head* to work correctly with your dataset\n",
+ "1. Use the number of epochs requested when calling the method to fit the entire model, updating the weights of the later layers (especially the head) faster than the earlier layers (which, as we'll see, generally don't require many changes from the pretrained weights).\n",
+ "\n",
+ "The *head* of a model is the part that is newly added to be specific to the new dataset. An *epoch* is one complete pass through the dataset. After calling `fit`, the results after each epoch are printed, showing the epoch number, the training and validation set losses (the \"measure of performance\" used for training the model), and any *metrics* you've requested (error rate, in this case)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Metric and Loss: A *metric* is a calculation that is made after each epoch and displayed so that you can see how well your model is training. It's not used as part of the actual learning process. The *loss* is the \"measure of performance\" that is used by the learning process to define whether one set of parameters is better or worse than another; the learning process works to make the loss as low as possible."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### What our image recognizer learned"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At this stage we have an image recogniser that is working very well, but we have no idea what it is actually doing! Although many people complain that deep learning results in impenetrable \"black box\" models, this really couldn't be further from the truth. There is a vast body of research showing how to deeply inspect deep learning models, and get rich insights from them.\n",
+ "\n",
+ "In 2013 a PhD student, Matt Zeiler, and his supervisor, Rob Fergus, published the paper [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901.pdf), which showed how to visualise the neural network weights learned in each layer of a model. They carefully analysed the model that won the 2012 ImageNet competition, and used this analysis to greatly improve the model, such that they were able to go on to win the 2013 competition! Here is the picture that they published of the first two layers' weights:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Activations](\"images/chapter2_layer1and2.PNG\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This picture requires some explanation. For each layer, the image part with the light grey background shows the reconstructed weights pictures, and the other section shows the parts of the training images which most strongly matched each set of weights. For layer 1, what we can see is that the model has discovered weights which represent diagonal, horizontal, and vertical edges, as well as various different gradients. (Note that for each layer only a subset of the features are shown; in practice there are thousands across all of the layers.) These are the basic building blocks that it has created automatically for computer vision. They have been widely analysed by neuroscientists and computer vision researchers, and it turns out that these learned building blocks are very similar to the basic visual machinery in the human eye, as well as the handcrafted computer vision features that were developed prior to the days of deep learning.\n",
+ "\n",
+ "For layer 2, there are nine examples of weight reconstructions for each of the features found by the model. We can see that the model has learned to create feature detectors that look for corners, repeating lines, circles, and other simple patterns. These are built from the basic building blocks developed in the first layer. For each of these, the right-hand side of the picture shows small patches from actual images which these features most closely match. For instance, the particular pattern in row 2 column 1 matches the gradients and textures associated with sunsets.\n",
+ "\n",
+ "Here is the image from the paper showing the results of reconstructing the features of layer 3:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Activations](\"images/chapter2_layer3.PNG\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see by looking at the right-hand side of this picture, the features are now able to identify and match with higher levels semantic components, such as car wheels, text, and flower petals. Using these components layers four and five can identify even higher-level concepts:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Activations](\"images/chapter2_layer4and5.PNG\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This article was studying an older model called `AlexNet` that only contained five layers. Networks developed since then can have hundreds of layers--so you can imagine how rich the features developed by these models can be!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### What image recognizers can do"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "An image recogniser can, as its name suggests, only recognise images. But a lot of things can be represented as images, which means that an image recogniser can learn to complete many tasks.\n",
+ "\n",
+ "For instance, a sound can be converted to a spectrogram, which is a chart that shows the amount of each frequency at each time in an audio file. Fast.ai student Ethan Sutin used this approach to easily beat the published accuracy on [environmental sound detection](https://medium.com/@etown/great-results-on-audio-classification-with-fastai-library-ccaf906c5f52) using a dataset of 8732 urban sounds. fastai's `show_batch` clearly shows how each different sound has a quite distinctive spectrogram:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"show_batch](\"images/att_00012.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Time series can be easily converted into an image by simply plotting the time series in the usual way. However, it is often a good idea to try to represent your data in a way that makes it as easy as possible to pull out the most important components. In a time-series, things like seasonality and anomalies are most likely to be of interest. There are various transformations available for time series data; for instance, fast.ai student Ignacio Oguiza created images from a time series data set for olive oil classification. He used a technique called Gramian Angular Field (GAF), and then fed those images to an image classification model just like the one you see in this chapter. His results, despite having only 30 training set images, were well over 90% accurate, and close to the state-of-the-art."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Converting](\"images/att_00013.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another interesting fast.ai student project example comes from Gleb Esman. He was working on fraud detection at Splunk, and was working with a dataset of users' mouse movements and mouse clicks. He turned these into pictures by drawing an image where the position, speed and acceleration of the mouse was displayed using coloured lines, and the clicks were displayed using [small coloured circles](https://www.splunk.com/en_us/blog/security/deep-learning-with-splunk-and-tensorflow-for-security-catching-the-fraudster-in-neural-networks-with-behavioral-biometrics.html). He then fed this into an image recognition model just like the one we've shown in this chapter, and it worked so well that had led to a patent for this approach to fraud analytics!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Converting](\"images/att_00014.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another examples comes from the paper [Malware Classification with Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/abstract/document/8328749) which explains that \"the malware binary file isdivided into 8-bit sequences which are then converted to equivalent decimalvalues. This decimal vector is reshaped and gray-scale image is generated thatrepresent the malware sample\":"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Malware](\"images/att_00055.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "They then show \"pictures\" generated through this process of malware in different categories:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Malware](\"images/att_00056.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, the different types of malware look very distinctive to the human eye. The model they train based on this image representation was more accurate at malware classification than any previous approach shown in the academic literature. This suggests a good rule of thumb for converting a dataset into an image representation: if the human eye can recognize categories from the images, then a deep learning model should be able to do so too.\n",
+ "\n",
+ "In general, you'll find that a small number of general approaches in deep learning can go a long way, if you're a bit creative in how you represent your data! You shouldn't think of approaches like the above as \"hacky workarounds\", since actually they often (as here) beat previously state of the art results. These really are the right way to think about these problem domains."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### A bit more jargon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's a summary of the deep learning vocabulary we've met so far:\n",
+ "\n",
+ "```asciidoc\n",
+ "[[dljargon]]\n",
+ ".Deep learning vocabulary\n",
+ "[options=\"header\"]\n",
+ "|=====\n",
+ "| Term | Meaning\n",
+ "|**label** | The data that we're trying to predict, such as \"dog\" or \"cat\"\n",
+ "|**architecture** | The _template_ of the model that we're trying to fit; the actual mathematical function that we're passing the input data and parameters to\n",
+ "|**model** | the combination of the architecture with a particular set of parameters\n",
+ "|**parameters** | the values in the model that change what task it can do, and are updated through model training\n",
+ "|**fit** | Update the parameters of the model such that the predictions of the model using the input data match the target labels\n",
+ "|**train** | A synonym for _fit_\n",
+ "|**pretrained model** | A model that has already been trained, generally using a large dataset, and will be fine-tuned\n",
+ "|**fine tune** | Update a pretrained model for a different task\n",
+ "|**epoch** | One complete pass through the input data\n",
+ "|**metric** | A measurement of how good the model is, using the validation set\n",
+ "|**validation set** | A set of data held out from training, used only for measuring how good the model is\n",
+ "|**training set** | The data used for fitting the model; does not include any data from the validation set\n",
+ "|**overfitting** | Training a model in such a way that it _remembers_ specific features of the input data, rather than generalizing well to data not seen during training\n",
+ "|**CNN** | Convolutional neural network; a type of neural network that works particularly well for computer vision tasks\n",
+ "|=====\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With this vocabulary in hand, we are now in a position to bring together all the key concepts so far. Take a moment to review those definitions and read the following summary. If you can follow the explanation, then you have laid down the basic coordinates for understanding many discussions to come.\n",
+ "\n",
+ "*Deep learning* is a specialty within *machine learning*, a discipline where we define a program not by writing it entirely ourselves but by using data. *Image classification* is a representative example. We start with *labeled data*, that is, a set of images where we have assigned a *label* to each image indicating what it represents. Our goal is to produce a program, called a *model*, which, given a new image, will make an accurate *prediction* regarding what that new image represents.\n",
+ "\n",
+ "Every model starts with a choice of *architecture*, a general template for how that kind of model works internally. The process of *training* (or *fitting*) the model is the process of finding a set of *parameter values* (or *weights*) which specializes that general architecture into a model that works well for our particular kind of data. In order to define how well a model does on a single prediction, we need to define a *loss function*, which defines how we score a prediction as good or bad.\n",
+ "\n",
+ "In order to make the training process go faster, we might start with a *pretrained model*, a model which has already been trained on someone else's data. We then adapt it to our data by training it a bit more on our data, a process called *fine tuning*.\n",
+ "\n",
+ "When we train a model, a key concern is to ensure that our model *generalizes* -- that is, that it learns general lessons from our data which also apply to new items it will encounter, so that it can make good predictions on those items. The risk is that if we train our model badly, instead of learning general lessons it effectively memorizes what it has already seen, and then it will make poor predictions about new images. Such a failure is called *overfitting*. In order to avoid this, we always divide our data into two parts, the *training set* and the *validation set*. We train the model by showing it only the *training set* and then we evaluate how well the model is doing by seeing how well it predicts on items from the *validation set* . In this way, we check if the the lessons the model learns from the training set are lessons that generalize to the validation set. In order to assess how well the model is doing on the validation set overall, we define a *metric* . During the training process, when the model has seen every item in the training set, we call that an *epoch* .\n",
+ "\n",
+ "All these concepts apply to machine learning in general. That is, they apply to all sorts of schemes for defining a model by training it with data. What makes deep learning distinctive is a particular class of architectures, the architectures based on *neural networks*. In particular, tasks like image classification rely heavily on *convolutional neural networks*, which we will discuss shortly."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deep learning is not just for image classification"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Deep learning's effectiveness for classifying images has been widely discussed in recent years, even showing _super-human_ results on complex tasks like recognizing malignant tumours in CT scans. But it can do a lot more than this, as we will show here.\n",
+ "\n",
+ "For instance, let's talk about something that is critically important for autonomous vehicles: localising objects in a picture. If a self-driving car doesn't know where a pedestrian is, then it doesn't know how to avoid one! Creating a model which can recognize the content of every individual pixel in an image is called *segmentation*. Here is how we can train a segmentation model using fastai, using a subset of the *Camvid* dataset from the paper [Semantic Object Classes in Video: A High-Definition Ground Truth Database](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "2.906601 | \n", + "2.347491 | \n", + "00:02 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "1.988776 | \n", + "1.765969 | \n", + "00:02 | \n", + "
| 1 | \n", + "1.703356 | \n", + "1.265247 | \n", + "00:02 | \n", + "
| 2 | \n", + "1.591550 | \n", + "1.309860 | \n", + "00:02 | \n", + "
| 3 | \n", + "1.459745 | \n", + "1.102660 | \n", + "00:02 | \n", + "
| 4 | \n", + "1.324229 | \n", + "0.948472 | \n", + "00:02 | \n", + "
| 5 | \n", + "1.205859 | \n", + "0.894631 | \n", + "00:02 | \n", + "
| 6 | \n", + "1.102528 | \n", + "0.809563 | \n", + "00:02 | \n", + "
| 7 | \n", + "1.020853 | \n", + "0.805135 | \n", + "00:02 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.show_results(max_n=6, figsize=(7,8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One othere area where deep learning has dramatically improved in the last couple of years is natural language processing (NLP). Computers can now generate text, translate automatically from one language to another, analyze comments, label words in sentences, and much more. Here is all of the code necessary to train a model which can classify the sentiment of a movie review better than anything that existed in the world just five years ago:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#id training2\n",
+ "#caption Training loop in a text application\n",
+ "from fastai2.text.all import *\n",
+ "\n",
+ "dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test')\n",
+ "learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)\n",
+ "learn.fine_tune(4, 1e-2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model is using the the IMDb dataset from the paper [Learning Word Vectors for Sentiment Analysis]((http://ai.stanford.edu/~amaas/data/sentiment/)). It works well with movie reviews of many thousands of words. But let's test it out on a very short one, to see it do its thing:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "('pos', tensor(1), tensor([0.0041, 0.9959]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn.predict(\"I really liked that movie!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now it's your turn! Write your own mini movie review, or copy one from the Internet, and we can see what this model thinks about it. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: The order matter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In a Jupyter notebook, the order in which you execute each cell is very important. It's not like Excel, where everything gets updated as soon as you type something anywhere, but it has an inner state that gets updated each time you execute a cell. For instance, when you run the first cell of the notebook (with the CLICK ME comment), you create an object `learn` that contains a model and data for an image classification problem. If we were to run the cell right above (the one that predicts if a review is good or not) straight after, we would get an error as this `learn` object does not contain a text classification model. This cell needs to be run after the one containing \n",
+ "\n",
+ "```python\n",
+ "from fastai2.text.all import *\n",
+ "\n",
+ "dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test')\n",
+ "learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)\n",
+ "learn.fine_tune(4, 1e-2)\n",
+ "```\n",
+ "\n",
+ "The outputs themselves can be deceiving: they have the results of the last time the cell was executed, but if you change the code inside a cell without executing it, you will keep them.\n",
+ "\n",
+ "Except when we mention it explicitely, the notebooks provided on the book website are meant to be run in order, from top to bottom. In general, when experimenting, you will find yourself executing cells in any order to go fast (which is a super neat featur of Jupyter Notebooks) but once you have explored and arrive at the final version of your code, make sure you can run the cells of your notebooks in order (your future self won't necessarily remember the convoluted path you took otherwise!). \n",
+ "\n",
+ "In edit mode, pressing `0` twice will restart the *kernel* (which is the engine powering your notebook). This will wipe your state clean and make it as if you had just started in the notebook. Clean then on the \"Cell\" menu and then on \"Run All Above\" to run all the cells above the point you are. We have found this to be very useful when developing the fastai library."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you ever have any questions about a fastai method, you should use the function `doc`:\n",
+ "\n",
+ "```python\n",
+ "doc(learn.predict)\n",
+ "```\n",
+ "\n",
+ "This will make a small window pop with a content like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "
\n",
+ "\n",
+ "> "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "from IPython.display import display, HTML, Markdown\n",
+ "md = show_doc(learn.predict, disp=False)\n",
+ "md += f'\\n\\nShow in docs'\n",
+ "display(Markdown(md))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A brief one-line explanation is provided by `doc`. The *show in docs* link is where you'll find all the details in the [full documentation](https://docs.fast.ai/), and lots of examples. Also, most of fastai's methods are just a handful of lines, so you can click the *source* link to see exactly what's going on behind the scenes.\n",
+ "\n",
+ "Let's move on to something much less sexy, but perhaps significantly more widely commercially useful: building models from plain *tabular* data. It turns out that looks very similar too. Here is the code necessary to train a model which will predict whether a person is a high-income earner, based on their socio-economic background:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Tabular: Data that is in the form of a table, such as from a spreadsheet, database, or CSV file. A tabular model is a model which tries to predict one column of a table based on information in other columns of a table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.tabular.all import *\n",
+ "path = untar_data(URLs.ADULT_SAMPLE)\n",
+ "\n",
+ "dls = TabularDataLoaders.from_csv(path/'adult.csv', path, y_names=\"salary\",\n",
+ " cat_names = ['workclass', 'education', 'marital-status', 'occupation',\n",
+ " 'relationship', 'race'],\n",
+ " cont_names = ['age', 'fnlwgt', 'education-num'],\n",
+ " procs = [Categorify, FillMissing, Normalize])\n",
+ "\n",
+ "learn = tabular_learner(dls, metrics=accuracy)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you see, we had to tell fastai which columns are *categorical* (that is, they contain values that are one of a discrete set of choices, such as `occupation`), versus *continuous* (that is, they contain a number that represents a quantity, such as `age`).\n",
+ "\n",
+ "There is no pretrained model available for this task (in general, pretrained models are not widely available for any tabular modeling tasks, although some organizations have created them for internal use), so we don't use `fine_tune` in this case, but instead `fit_one_cycle`, the most commonly used method for training fastai models *from scratch* (i.e. without transfer learning):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model is using the *adult* dataset, from the paper [Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid](https://archive.ics.uci.edu/ml/datasets/adult), which contains some data regarding individuals (like their education, marital status, race, sex, etc.) and whether or not they have an annual income greater than \\$50k. The model is over 80\\% accurate, and took around 30 seconds to train.\n",
+ "\n",
+ "Let's look at one more. Recommendation systems are very important, particularly in e-commerce. Companies like Amazon and Netflix try hard to recommend products or movies which you might like. Here's how to train a model which will predict which people might like which movie, based on their previous viewing habits, using the [MovieLens dataset](http://dx.doi.org/10.1145/2827872):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from fastai2.collab import *\n",
+ "path = untar_data(URLs.ML_SAMPLE)\n",
+ "dls = CollabDataLoaders.from_csv(path/'ratings.csv')\n",
+ "learn = collab_learner(dls, y_range=(0.5,5.5))\n",
+ "learn.fine_tune(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model is predicting movie ratings on a scale of 0.5 to 5.0 to within around 0.6 average error. Since we're predicting a continuous number, rather than a category, we have to tell fastai what range our target has, using the `y_range` parameter.\n",
+ "\n",
+ "Although we're not actually using a pretrained model (for the same reason that we didn't for the tabular model), this example shows that fastai let's us use `fine_tune` even in this case (we'll learn how and why this works later in the book). We can use the same `show_results` call we saw earlier to view a few examples of user and movie IDs, actual ratings, and predictions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.show_results()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: Datasets: food for models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You’ve already seen in this section quite a few models, each one trained using a different dataset, to do a different task. In machine learning and deep learning, we can’t do anything without data. So, the people that create datasets for us to train our models are the (often under-appreciated) heroes. Some of the most useful and important datasets are those that become important *academic baselines*; that is, datasets that are widely studied by researchers and used to compare algorithmic changes. Some of these become household names (at least, among households that train models!), such as MNIST, CIFAR 10, and ImageNet.\n",
+ "\n",
+ "The datasets used in this book have been selected because they provide great examples of the kind of data that you are likely to encounter, and the academic literature has many examples of model results using these datasets which you can compare your work to.\n",
+ "\n",
+ "Most datasets used in this book took the creators a lot of work to build. For instance, later in the book we’ll be showing you how to create a model that can translate between French and English. The key input to this is a French/English parallel text corpus prepared back in 2009 by Professor Chris Callison-Burch of the University of Pennsylvania. This dataset contains over 20 million sentence pairs in French and English. He built the dataset in a really clever way: by crawling millions of Canadian web pages (which are often multi-lingual) and then using a set of simple heuristics to transform French URLs onto English URLs.\n",
+ "\n",
+ "As you look at datasets throughout this book, think about where they might have come from, and how they might have been curated. Then, think about what kinds of interesting dataset you could create for your own projects. (We’ll even take you step by step through the process of creating your own image dataset soon.)\n",
+ "\n",
+ "fast.ai has spent a lot of time creating cutdown versions of popular datasets that are specially designed to support rapid prototyping and experimentation, and to be easier to learn with. In this book we will often start by using one of the cutdown versions, and were later on scale up to the full-size version (just as we're doing in this chapter!) In fact, this is how the world’s top practitioners do their modelling projects in practice; they do most of their experimentation and prototyping with subsets of their data, and only use the full dataset when they have a good understanding of what they have to do."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Validation sets and test sets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we've discussed, the goal of a model is to make predictions about data. But the model training process is fundamentally dumb. If we trained a model with all our data, and then evaluated the model using that same data, we would not be able to tell how well our model can perform on data it hasn’t seen. Without this very valuable piece of information to guide us in training our model, there is a very good chance it would become good at making predictions about that data but would perform poorly on new data.\n",
+ "\n",
+ "It is in order to avoid this that our first step was to split our dataset into two sets, the training set (which our model sees in training) and the validation set (which is used only for evaluation). This lets us test that the model learns lessons from the training data which generalize to new data, the validation data.\n",
+ "\n",
+ "One way to understand this situation is that, in a sense, we don't want our model to get good results by \"cheating\". If it predicts well on a data item, that should be because it has learned principles that govern that kind of item, and not because the model has been shaped by *actually having seeing that particular item*.\n",
+ "\n",
+ "Splitting off our validation data means our model never sees it in training, and so is completely untainted by it, and is not cheating in any way. Right?\n",
+ "\n",
+ "In fact, not necessarily. The situation is more subtle. The subtlety is that in realistic scenarios we rarely build a model just by training its weight parameters once. Instead we are likely to explore many versions of a model through various modelling choices regarding network architecture, learning rates, data augmentation strategies, and other factors we will discuss in upcoming chapters. Many of these choices can be described as choices of *hyperparameters*. The word reflects that they are parameters about parameters, since they are the higher-level choices that govern the meaning of the weight parameters."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The problem is that, even though the ordinary training process is only looking at predictions on the training data when it learns values for the weight parameters, the same is not true about us. We, as modellers, are evaluating the model by looking at predictions on the validation data, when we decide to explore new hyperparameter values! So subsequent versions of the model are, indirectly, shaped by having seen the validation data. Just as the automatic training process is in danger of overfitting the training data, we are in danger of overfitting the validation data, by human trial and error and exploration.\n",
+ "\n",
+ "The solution to this conundrum is to introduce another level of even more highly reserved data, the \"test set\". Just as we hold back the validation data from the training process, we must hold back the test set data even from ourselves. It cannot be used to improve the model; it can only be used to evaluate the model at the very end of our efforts. In effect, we define a hierarchy of cuts of our data, based on how fully we want to hide it from training and modelling processes -- training data is fully exposed, the validation data is less exposed, and test data is totally hidden. This hierarchy parallels the different kinds of modelling and evaluation processes themselves -- the automatic training process with back propagation, the more manual process of trying different hyper-parameters between training sessions, and the assessment of our final result.\n",
+ "\n",
+ "Having two levels of \"reserved data\", a validation set and a test set -- with one level representing data which you are virtually hiding from yourself -- may seem a bit extreme. But the reason it is often necessary is because models tend to gravitate toward the simplest way to do good predictions (memorization), and we as fallible humans tend to gravitate toward fooling ourselves about how well our models are performing. The discipline of the test set helps us keep ourselves intellectually honest.\n",
+ "\n",
+ "This same discipline can be critical if you intend to hire a third-party to perform modelling work on your behalf. A third-party might not understand your requirements accurately, or their incentives might even encourage them to misunderstand them. But a good test set can greatly mitigate these risks and let you evaluate if their work solves your actual problem.\n",
+ "\n",
+ "To put it bluntly, if you're a senior decision maker in your organization (or you're advising senior decision makers) then the most important takeaway is this: if you ensure that you really understand what a test set is, and why it's important, then you'll avoid the single biggest source of failures we've seen when organizations decide to use AI. For instance, if you're considering bringing in an external vendor or service, make sure that you hold out some test data that the vendor *never gets to see*. Then *you* check their model on your test data, using a metric that *you* choose based on what actually matters to you in practice, and *you* decide what level of performance is adequate. (It's also a good idea for you to try out some simple baseline yourself, so you know what a really simple model can achieve. Often it'll turn out that your simple model can be just as good as an external \"expert\"!)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use judgment in defining test sets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To do a good job of defining a validation set (and possibly a test set), you will sometimes want to do more than just randomly grab a fraction of your original datset. Remember: a key property of the validation and test sets is that they must be representative of the new data you will see in the future. This may sound like an impossible order! By definition, you haven’t seen this data yet. But you usually still do know some things.\n",
+ "\n",
+ "It's instructive to look at a few example cases. Many of these examples come from predictive modeling competitions on the *Kaggle* platform, which is a good representation of problems and methods you would see in practice.\n",
+ "\n",
+ "One case might be if you are looking at time series data. For a time series, choosing a random subset of the data will be both too easy (you can look at the data both before and after the dates your are trying to predict) and not representative of most business use cases (where you are using historical data to build a model for use in the future). If your data includes the date and you are building a model to use in the future, you will want to choose a continuous section with the latest dates as your validation set (for instance, the last two weeks or last month of the available data).\n",
+ "\n",
+ "Suppose you want to split the time series data below into training and validation sets:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"A](\"images/timeseries1.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A random subset is a poor choice (too easy to fill in the gaps, and not indicative of what you'll need in production):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Random](\"images/timeseries2.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use the earlier data as your training set (and the later data for the validation set):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Training](\"images/timeseries3.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For example, Kaggle had a competition to [predict the sales in a chain of Ecuadorian grocery stores](https://www.kaggle.com/c/favorita-grocery-sales-forecasting). Kaggle's *training data* ran from Jan 1 2013 to Aug 15 2017 and the test data spanned Aug 16 2017 to Aug 31 2017. That way, the competition organizer ensured that entrants were making predictions for a time period that was *in the future*, from the perspective of their model. This is similar to the way quant hedge fund traders do *back-testing* to check whether their models are predictive of future periods, based on passed data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After time series, a second common case is when you can easily anticipate ways the data you will be making predictions for in production may be *qualitatively different* from the data you have to train your model with.\n",
+ "\n",
+ "In the Kaggle [distracted driver competition](https://www.kaggle.com/c/state-farm-distracted-driver-detection), the independent data are pictures of drivers at the wheel of a car, and the dependent variable is a category such as texting, eating, or safely looking ahead. If you were the insurance company building a model from this data, note that you would be most interested in how the model performs on drivers you haven't seen before (since you would likely have training data only for a small group of people). This is true of the Kaggle competition as well: the test data consists of people that weren't used in the training set."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"A](\"images/driver_phone.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"A](\"images/driver_phone2.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you put one of the above images in your training set and one in the validation set, your model will seem to be performing better than it would on new people. Another perspective is that if you used all the people in training your model, your model may be overfitting to particularities of those specific people, and not just learning the states (texting, eating, etc).\n",
+ "\n",
+ "A similar dynamic was at work in the [Kaggle fisheries competition](https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring) to identify the species of fish caught by fishing boats in order to reduce illegal fishing of endangered populations. The test set consisted of boats that didn't appear in the training data. This means that you'd want your validation set to include boats that are not in the training set.\n",
+ "\n",
+ "Sometimes it may not be clear how your validation data will differ. For instance, for a problem using satellite imagery, you'd need to gather more information on whether the training set just contained certain geographic locations, or if it came from geographically scattered data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A _Choose Your Own Adventure_ moment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that you have got a taste of how to build a model, you can decide what you want to dig into next. If you would like to learn more about how to use deep learning models in practice, including identifying and fixing errors, and creating a real working web application, and how to avoid your model causing unexpected harm to your organization or society more generally, then keep reading the next chapters, _From model to production_, and _Data ethics_. If you would like to start learning the foundations of how deep learning works _under the hood_, skip to <>, _Under the hood: training a digit classifier_. (Did you ever read _Choose Your Own Adventure_ books as a kid? Well, this is kind of like that… except with more deep learning than that book series contained.)\n",
+ "\n",
+ "Either way, you will need to read all these chapters in order to progress further in the book; but it is totally up to you which order you read them in. They don't depend on each other. If you skip ahead to <>, then we will remind you at the end of that section to come back and read the chapters you skipped over before you go any further."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It can be hard to know in pages and pages of prose what are the key things you really need to focus on and remember. So we've prepared a list of questions and suggested steps to complete at the end of each chapter. All the answers are in the text of the chapter, so if you're not sure about anything here, re-read that part of the text and make sure you understand it. Answers to all these questions are also available on the [book website](https://book.fast.ai). You can also visit [the forums](https://forums.fast.ai) if you get stuck to get help from other folks studying this material."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Do you need these for deep learning?\n",
+ " - Lots of math T / F\n",
+ " - Lots of data T / F\n",
+ " - Lots of expensive computers T / F\n",
+ " - A PhD T / F\n",
+ "1. Name five areas where deep learning is now the best in the world\n",
+ "1. What was the name of the first device that was based on the principle of the artificial neuron?\n",
+ "1. Based on the book of the same name, what are the either requirements for \"Parallel Distributed Processing\"?\n",
+ "1. What were the two theoretical misunderstandings that held back the field of neural networks?\n",
+ "1. What is a GPU?\n",
+ "1. Open a notebook and execute a cell containing: `1+1`. What happens?\n",
+ "1. Follow through each cell of the stripped version of the notebook for this chapter. Before executing each cell, guess what will happen.\n",
+ "1. Complete the Jupyter Notebook online appendix.\n",
+ "1. Why is it hard to use a traditional computer program to recognize images in a photo?\n",
+ "1. What did Samuel mean by \"Weight Assignment\"?\n",
+ "1. What term do we normally use in deep learning for what Samuel called \"Weights\"?\n",
+ "1. Draw a picture that summarizes Arthur Samuel's view of a machine learning model\n",
+ "1. Why is it hard to understand why a deep learning model makes a particular prediction?\n",
+ "1. What is the name of the theorem that a neural network can solve any mathematical problem to any level of accuracy?\n",
+ "1. What do you need in order to train a model?\n",
+ "1. How could a feedback loop impact the rollout of a predictive policing model?\n",
+ "1. Do we always have to use 224x224 pixel images with the cat recogition model?\n",
+ "1. What is the difference between classification and regression?\n",
+ "1. What is a validation set? What is a test set? Why do we need them?\n",
+ "1. What will fastai do if you don't provide a validation set?\n",
+ "1. Can we always use a random sample for a validation set? Why or why not?\n",
+ "1. What is overfitting? Provide an example.\n",
+ "1. What is a metric? How does it differ to \"loss\"?\n",
+ "1. How can pretrained models help?\n",
+ "1. What is the \"head\" of a model?\n",
+ "1. What kinds of features do the early layers of a CNN find? How about the later layers?\n",
+ "1. Are image models only useful for photos?\n",
+ "1. What is an \"architecture\"?\n",
+ "1. What is segmentation?\n",
+ "1. What is `y_range` used for? When do we need it?\n",
+ "1. What are \"hyperparameters\"?\n",
+ "1. What's the best way to avoid failures when using AI in an organization?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each chapter also has a \"further research\" with questions that aren't fully answered in the text, or include more advanced assignments. Answers to these questions aren't on the book website--you'll need to do your own research!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Why is a GPU useful for deep learning? How is a CPU different, and why is it less effective for deep learning?\n",
+ "1. Try to think of three areas where feedback loops might impact use of machine learning. See if you can find documented examples of that happening in practice."
+ ]
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/02_production.ipynb b/02_production.ipynb
new file mode 100644
index 000000000..1194da94a
--- /dev/null
+++ b/02_production.ipynb
@@ -0,0 +1,1792 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *\n",
+ "from fastai2.vision.widgets import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_production]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# From model to production"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The five lines of code we've seen are just one small part of the process of using deep learning in practice. In this section, we're going to use a computer vision example to look at the end-to-end process of creating a deep learning application. More specifically: we're going to build a bear classifier! In the process, we'll discuss the capabilities and constraints of deep learning, learn about how to create datasets, look at possible gotchas when using deep learning in practice, and more."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Picking a problem"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've seen that deep learning can solve a lot of challenging problems quickly and with little code. However, deep learning isn't magic! We often talk to people who overestimate both the constraints, and the capabilities of deep learning. Both of these can be problems: underestimating the capabilities means that you might not even try things which could be very beneficial; underestimating the constraints might mean that you fail to consider and react to important issues.\n",
+ "\n",
+ "The best thing to do is to keep an open mind. If you remain open to the possibility that deep learning might solve part of your problem with less data or complexity than you expect, then it is possible to design a process where you can find the specific capabilities and constraints related to your particular problem as you work through the process. This doesn't mean making any risky bets — we will show you how you can gradually roll out models so that they don't create significant risks, and can even backtest them prior to putting them in production."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The state of deep learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In general, here is a summary of the state of deep learning is at the start of 2020. However, things move very fast, and by the time you read this some of these constraints may no longer exist. We will try to keep the book website up-to-date; in addition, a Google search for \"what can AI do now\" there is likely to provide some up-to-date information."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Computer vision**: there are many domains in which deep learning has not been used to analyse images yet, but those where it has been tried have nearly universally shown that computers can recognise what items are in an image at least as well as people can — even specially trained people, such as radiologists. This is known as *object recognition*. Deep learning is also good at recognizing whereabouts objects in an image are, and can highlight their location and name each found object. This is known as *object detection* (there is also a variant of this we saw in <>, where every pixel is categorized based on what kind of object it is part of--this is called *segmentation*). Deep learning algorithms are generally not good at recognizing images that are significantly different in structure or style to those used to train the model. For instance, if there were no black-and-white images in the training data, the model may well do poorly on black-and-white images. If the training data did not contain hand-drawn images then the model will probably do poorly on hand-drawn images. There is no general way to check what types of image are missing in your training set, but we will show in this chapter some ways to try to recognize when unexpected image types arise in the data when the model is being used in production (this is known as checking for *out of domain* data).\n",
+ "\n",
+ "One major challenge for object detection systems is that image labelling can be slow and expensive. There is a lot of work at the moment going into tools to try to make this labelling faster and more easy, and require less handcrafted labels to train accurate object detection models. One approach which is particularly helpful is to synthetically generate variations of input images, such as by rotating them, or changing their brightness and contrast; this is called *data augmentation* and also works well for text and other types of model. We will be discussing it in detail in this chapter.\n",
+ "\n",
+ "Another point to consider is that although your problem might not look like a computer vision problem, it might be possible with a little imagination to turn it into one. For instance, if what you are trying to classify is sounds, you might try converting the sounds into images of their acoustic waveforms and then training a model on those images."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Text (natural language processing)**: just like in computer vision, computers are very good at categorising both short and long documents based on categories such as spam, sentiment, author, source website, and so forth. We are not aware of any rigourous work done in this area to compare to human performance, but anecdotally it seems to us that deep learning performance is similar to human performance here. Deep learning is also very good at generating context-appropriate text, such as generating replies to social media posts, and imitating a particular author's style. It is also good at making this content compelling to humans, and has been shown to be even more compelling than human-generated text. However, deep learning is currently not good at generating *correct* responses! We don't currently have a reliable way to, for instance, combine a knowledge base of medical information, along with a deep learning model for generating medically correct natural language responses. This is very dangerous, because it is so easy to create content which appears to a layman to be compelling, but actually is entirely incorrect.\n",
+ "\n",
+ "Another concern is that context-appropriate, highly compelling responses on social media can be used at massive scale — thousands of times greater than any troll farm previously seen — to spread disinformation, create unrest, and encourage conflict. As a rule of thumb, text generation will always be technologically a bit ahead of the ability of models to recognize automatically generated text. For instance, as we will see in this book, it is possible to use a model that can recognize artificially generated content to actually improve the generator that creates that content, until the classification model is no longer able to complete its task.\n",
+ "\n",
+ "Despite these issues, deep learning can be used to translate text from one language to another, summarize long documents into something which can be digested more quickly, find all mentions of a concept of interest, and so forth. Unfortunately, the translation or summary could well include completely incorrect information! However, it is already good enough that many people are using the systems — for instance Google's online translation system (and every other online service we are aware of) is based on deep learning."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Combining text and images**: the ability of deep learning to combine text and images into a single model is, generally, far better than most people intuitively expect. For example, a deep learning model can be trained on input images, and output captions written in English, and can learn to generate surprisingly appropriate captions automatically for new images! But again, we have the same warning that we discussed in the previous section: there is no guarantee that these captions will actually be correct.\n",
+ "\n",
+ "Because of this serious issue we generally recommend that deep learning be used not as a entirely automated process, but as part of a process in which the model and a human user interact closely. This can potentially make humans orders of magnitude more productive than they would be with entirely manual methods, and actually result in more accurate processes than using a human alone. For instance, an automatic system can be used to identify potential strokes directly from CT scans, send a high priority alert to have potential/scans looked at quickly. There is only a three-hour window to treat strokes, so this fast feedback loop could save lives. At the same time, however, all scans could continue to be sent to radiologists in the usual way, so there would be no reduction in human input. Other deep learning models could automatically measure items seen on the scan, and insert those measurements into report, warn the radiologist about findings that they may have missed, and tell the radiologist about other cases which might be relevant."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Tabular**: for analysing timeseries and tabular data, deep learning has recently been making great strides. However, deep learning is generally used as part of a ensemble of multiple types of model. If you already have a system that is using random forests or gradient boosting machines (popular tabular modelling tools that we will learn about soon) then switching to, or adding, deep learning may not result in any dramatic improvement. Deep learning does greatly increase the variety of columns that you can include, for example columns containing natural language (e.g. book titles, reviews, etc), and *high cardinality categorical* columns (i.e. something that contains a large number of discrete choices, such as zip code or product id). On the downside, deep learning models generally take longer to train than random forests or gradient boosting machines, although this is changing thanks to libraries such as [RAPIDS](https://rapids.ai/), which provides GPU acceleration for the whole modeling pipeline. We cover the pros and cons of all these methods in detail in <> in this book."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Recommendation systems**: Recommendation systems are really just a special type of tabular data. In particular, they generally have a high cardinality categorical variable representing users, and another one representing products (or something similar). A company like Amazon represents every purchase that has ever been made as a giant sparse matrix, with customers as the rows and products as the columns. Once they have the data in this format, data scientists apply some form of collaborative filtering to *fill in the matrix*. For example, if customer A buys products 1 and 10, and customer B buys products 1, 2, 4, and 10, the engine will recommend that A buy 2 and 4. Because deep learning models are good at handling high cardinality categorical variables they are quite good at handling recommendation systems. They particularly come into their own, just like for tabular data, when combining these variables with other kinds of data, such as natural language, or images. They can also do a good job of combining all of these types of information additional meta data represented as tables, such as user information, previous transactions, and so forth.\n",
+ "\n",
+ "However, nearly all machine learning approaches have the downside that the only tell you what products a particular user might like, rather than what recommendations would be helpful for a user. Many kinds of recommendations for products a user might like may not be at all helpful, for instance, if the user is already familiar with its products, or if they are simply different packagings of products they have already purchased (such as a boxed set of novels, where they already have each of the items in that set). Jeremy likes reading books by Terry Pratchett, and for a while Amazon was recommending nothing but Terry Pratchett books to him, which really wasn't helpful because he already was aware of these books!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Terry](\"images/pratchett.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Other data types**: Often you will find that domain-specific data types fit very nicely into existing categories. For instance, protein chains look a lot like natural language documents, in that they are long sequences of discrete tokens with complex relationships and meaning throughout the sequence. And indeed, it does turn out the using NLP deep learning methods is the current state of the art approach for many types of protein analysis. As another example: sounds can be represented as spectrograms, which can be treated as images; standard deep learning approaches for images turn out to work really well on spectrograms."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The Drivetrain approach"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are many accurate models that are of no use to anyone, and many inaccurate models that are highly useful. To ensure that your modeling work is useful in practice, you need to consider how your work will be used. In 2012 Jeremy, along with Margit Zwemer and Mike Loukides, introduced a method called *The Drivetrain Approach* for thinking about this issue, which we will summarize here. For more information, see the full article on oreilly.com [Designing Great Data Products](https://www.oreilly.com/radar/drivetrain-approach-data-products/).\n",
+ "\n",
+ "Consider a model in an autonomous vehicle, you want to help a car drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it doesn't stand on its own; as products become more sophisticated, it disappears into the plumbing. Someone using a self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach.\n",
+ "\n",
+ "We use data not just to generate more data (in the form of predictions), but to produce *actionable outcomes*. That is the goal of the Drivetrain Approach. Start by defining a clear **objective**. For instance, Google, when creating their first search engine, considered \"What is the user’s main objective in typing in a search query?\", and their answer was \"show the most relevant search result\". The next step is to consider what **levers** you can pull (i.e. what actions could you take) to better achieve that objective. In Google's case, that was the ranking of the search results. The third step was to consider what new **data** they would need to produce such a ranking; they realized that the implicit information regarding which pages linked to which other pages could be used for this purpose. Only after these first three steps do we begin thinking about building the predictive **models**. Our objective and available levers, what data we already have and what additional data we will need to collect, determine the models we can build. The models will take both the levers and any uncontrollable variables as their inputs; the outputs from the models can be combined to predict the final state for our objective."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"images/drivetrain-approach.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's consider another example: recommendation systems. The **objective** of a recommendation engine is to drive additional sales by surprising and delighting the customer with recommendations of items they would not have purchased without the recommendation. The **lever** is the ranking of the recommendations. New **data** must be collected to generate recommendations that will *cause new sales*. This will require conducting many randomized experiments in order to collect data about a wide range of recommendations for a wide range of customers. This is a step that few organizations take; but without it, you don't have the information you need to actually optimize recommendations based on your true objective (more sales!)\n",
+ "\n",
+ "Finally, you could build two **models** for purchase probabilities, conditional on seeing or not seeing a recommendation. The difference between these two probabilities is a utility function for a given recommendation to a customer. It will be low in cases where the algorithm recommends a familiar book that the customer has already rejected (both components are small) or a book that he or she would have bought even without the recommendation (both components are large and cancel each other out).\n",
+ "\n",
+ "As you can see, in practice often the practical implementation of your model will require a lot more than just training a model! You'll often need to run experiments to collect more data, and consider how to incorporate your models into the overall system you're developing."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Starting your project"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So where should you start your deep learning journey? The most important thing is to ensure that you have some project that you are working on — it is only through working on your own projects that you will get real experience of building and using models. When selecting a project, the most important consideration is data availability. Regardless of whether you are doing a project just for your own learning, or for practical application in your organization, you want something where you can get started quickly. We have seen many students, researchers, and industry practitioners waste months or years while they attempt to find their perfect dataset. The goal is not to find the perfect dataset, or the perfect project, but just to get started, and iterate from there.\n",
+ "\n",
+ "If you take this approach, then you will be on your third iteration of learning and improving whilst the perfectionists are still in the planning stages!\n",
+ "\n",
+ "We also suggest that you iterate from end to end in your project; that is, don't spend months fine tuning your model, or polishing the perfect GUI, or labelling the perfect dataset… Instead, complete every step as well as you can in a reasonable amount of time, all the way to the end. For instance, if your final goal is an application that runs on a mobile phone, then that should be what you have after each iteration. But perhaps in the early iterations you take some shortcuts, for instance by doing all of the processing on a remote server, and using a simple responsive web application. By completing the project and to end, you will see where the most tricky bits are, and which bits make the biggest difference to the final result."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you work through this book, we suggest that you both complete lots of small experiments, by running and adjusting the notebooks we provide, at the same time that you gradually develop your own projects. That way, you will be getting experience with all of the tools and techniques that were explaining, as we discuss them.\n",
+ "\n",
+ "> s: To make the most of this book, take the time to experiment between each chapter, be it on your own project or exploring the notebooks we provide. Then try re-writing those notebooks from scratch on a new dataset. It's only by practicing (and failing) a lot that you will get an intuition on how to train a model. \n",
+ "\n",
+ "By using the end to end iteration approach you will also get a better understanding of how much data you really need. For instance, you may find you can only easily get 200 labelled data items, and you can't really know until you try whether that's enough to get the performance you need for your application to work well in practice.\n",
+ "\n",
+ "In an organizational context you will be able to show your colleagues that your idea can really work, by showing them a real working prototype. We have repeatedly observed that this is the secret to getting good organizational buy in for a project."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since it is easiest to get started on a project where you already have data available, that means it's probably easiest to get started on a project related to something you are already doing, because you already have data about things that you are doing. For instance, if you work in the music business, you may have access to many recordings. If you work as a radiologist, you probably have access to lots of medical images. If you are interested in wildlife preservation, you may have access to lots of images of wildlife.\n",
+ "\n",
+ "Sometimes, you have to get a bit creative. Maybe you can find some previous machine learning project, such as a Kaggle competition, that is related to your field of interest. Sometimes, you have to compromize. Maybe you can't find the exact data you need for the precise project you have in mind; but you might be able to find something from a similar domain, or measured in a different way, tackling a slightly different problem. Working on these kinds of similar projects will still give you a good understanding of the overall process, and may help you identify other shortcuts, data sources, and so forth.\n",
+ "\n",
+ "Especially when you are just starting out with deep learning it's not a good idea to branch out into very different areas to places that deep learning has not been applied to before. That's because if your model does not work at first, you will not know whether it is because you have made a mistake, or if the very problem you are trying to solve is simply not solvable with deep learning. And you won't know where to look to get help. Therefore, it is best at first to start with something where you can find an example online of somebody who has had good results with something that is at least somewhat similar to what you are trying to achieve, or where you can convert your data into a format similar what someone else has used before (such as creating an image from your data). Have a look at the *state of deep learning* earlier in this chapter for a reminder of what kinds of things deep learning is good at right now."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Gathering data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For many types of projects, you may be able to find all the data you need online. The project we'll be completing in this chapter is a *bear detector*. It will discriminate between three types of bear: grizzly, black, and teddy bear. There are many images on the internet of each type of bear we can use. We just need a way to find them and download them. We've provided a tool you can use for this purpose, so you can follow along with this chapter, creating your own image recognition application for whatever kinds of object you're interested in. In the fast.ai course, thousands of students have presented their work on the course forums, displaying everything from Trinidad hummingbird varieties, to Panama bus types, and even an application that helped one student let his fiancee recognize his sixteen cousins during Christmas vacation!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To download images, you should sign up at Microsoft for *Bing Image Search*. You will be given a key, which you can either paste over `os.environ('AZURE_SEARCH_KEY')` below, or you can set in your terminal as:\n",
+ "\n",
+ " export AZURE_SEARCH_KEY=your_key_here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "key = os.environ['AZURE_SEARCH_KEY']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As at the time of writing, Bing Image Search is the best option we know of for finding and downloading images. It's free for up to 1000 queries per month, and each query can download up to 150 images. However, something better might have come along between when we wrote this and when you're reading the book, so be sure to check out [book.fast.ai](https://book.fast.ai) where we'll let you know our current recommendation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "150"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results = search_images_bing(key, 'grizzly bear')\n",
+ "ims = results.attrgot('content_url')\n",
+ "len(ims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've successfully downloaded the URLs of 150 grizzly bears (or, at least, images that Bing Image Search finds for that search term). Let's look at one:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "ims = ['http://3.bp.blogspot.com/-S1scRCkI3vY/UHzV2kucsPI/AAAAAAAAA-k/YQ5UzHEm9Ss/s1600/Grizzly%2BBear%2BWildlife.jpg']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dest = 'images/grizzly.jpg'\n",
+ "download_url(ims[0], dest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im = Image.open(dest)\n",
+ "im.to_thumb(128,128)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This seems to have worked nicely, so let's use fastai's `download_images` to download all the URLs from each of our search terms. We'll put each in a separate folder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bear_types = 'grizzly','black','teddy'\n",
+ "path = Path('bears')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if not path.exists():\n",
+ " path.mkdir()\n",
+ " for o in bear_types:\n",
+ " dest = (path/o)\n",
+ " dest.mkdir(exist_ok=True)\n",
+ " results = search_images_bing(key, f'{o} bear')\n",
+ " download_images(dest, urls=results.attrgot('content_url'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our folder has image files, as we'd expect:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#421) [Path('bears/black/00000095.jpg'),Path('bears/black/00000133.jpg'),Path('bears/black/00000062.jpg'),Path('bears/black/00000023.jpg'),Path('bears/black/00000029.jpg'),Path('bears/black/00000094.jpg'),Path('bears/black/00000124.jpg'),Path('bears/black/00000056.jpeg'),Path('bears/black/00000046.jpg'),Path('bears/black/00000045.jpg')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fns = get_image_files(path)\n",
+ "fns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: I just love this about working in Jupyter notebooks! It's so easy to gradually build what I want, and check my work every step of the way. I make a *lot* of mistakes, so this is really helpful to me..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Often when we download files from the internet, there's a few that are corrupt. Let's check:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(#0) []"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "failed = verify_images(fns)\n",
+ "failed"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: Getting help in jupyter notebooks"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Jupyter notebooks are great to easily experiment and immediately see the results of each function, but there is also a lot of functionality to help figure out how to use the functions you have or even directly look at their source code. For instance, if you type in a cell\n",
+ "```\n",
+ "??verify_images\n",
+ "```\n",
+ "a window will pop up with:\n",
+ "```\n",
+ "Signature: verify_images(fns)\n",
+ "Source: \n",
+ "def verify_images(fns):\n",
+ " \"Find images in `fns` that can't be opened\"\n",
+ " return L(fns[i] for i,o in\n",
+ " enumerate(parallel(verify_image, fns)) if not o)\n",
+ "File: ~/git/fastai/fastai/vision/utils.py\n",
+ "Type: function\n",
+ "```\n",
+ "It tells us what argument the function accepts (`fns`) then shows us the source code and the file it comes from. Looking at that source code, we can see it applies the function `verify_image` in parallel and only keep the ones for which the result of that function is `False`, which is consistent with the doc string: it finds the images in `fns` that can't be opened.\n",
+ "\n",
+ "Here are the commands that are very useful in jupyter notebooks:\n",
+ "\n",
+ "- at any point, if you don't remember the exact spelling of a function or argument name, you can press \"tab\" to get suggestions of auto-completion.\n",
+ "- when inside the parenthesis of a function, pressing \"shift\" and \"tab\" simultaneously will display a window with the signature of the function and a short documentation. Pressing it twice will expand the documentation and pressing it three times will open a full window with the same information at the bottom of your screen.\n",
+ "- in a cell, typing `?func_name` and executing will open a window with the signature of the function and a short documentation.\n",
+ "- in a cell, typing `??func_name` and executing will open a window with the signature of the function, a short documentation and the source code.\n",
+ "- if you are using the fasti library, we added a `doc` function for you, executing `doc(func_name)` in a cell will open a window with the signature of the function, a short documentation and links to the source code on GitHub and the full documentation of the funciton in the [documentation of the library](https://docs.fast.ai).\n",
+ "- unrelated to the documentation but still very useful to get help, at any point, if you get an error, type `%debug` in the next cell and execute to open the [python debugger](https://docs.python.org/3/library/pdb.html) that will let you inspect the content of every variable."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To remove the failed images, we can use `unlink` on each. Note that, like most fastai functions that return a collection, `verify_images` returns an object of type `L`, which includes the `map` method. This calls the passed function on each element of the collection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "failed.map(Path.unlink);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing to be aware of in this process: as we discussed in <>, models can only reflect the data used to train them. And the world is full of biased data, which ends up reflected in, for example, Bing Image Search (which we used to create our dataset). For instance, let's say you were interested in creating an app which could help users figure out whether they had healthy skin, so you trained a model on the results of searches for (say) *healthy skin*. Here's the results you would get:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"images/healthy_skin.gif\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So with this as your training data, you would end up not with a healthy skin detector, but a *young white woman touching her face* detector! Be sure to think carefully about the types of data that you might expect to see in practice in your application, and check carefully to ensure that all these types are reflected in your model's source data. (Thanks to Deb Raji, who came up with the *healthy skin* example. See her paper *Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products* for more fascinating insights into model bias.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From data to DataLoaders"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have downloaded and verified of the data that we want to use, we need to turn it into a `DataLoaders` object. `DataLoaders` is a thin class which just stores whatever `DataLoader` objects you pass to it, and makes them available as `train` and `valid` . Although it's a very simple class, it's very important in fastai: it provides the data for your model. The key functionality in `DataLoaders` is provided with just these 4 lines of code (it has some other minor functionality we'll skip over for now):\n",
+ "\n",
+ "```python\n",
+ "class DataLoaders(GetAttr):\n",
+ " def __init__(self, *loaders): self.loaders = loaders\n",
+ " def __getitem__(self, i): return self.loaders[i]\n",
+ " train,valid = add_props(lambda i,self: self[i])\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: DataLoaders: a fastai class which stores whatever `DataLoader` objects you pass to it, and makes them available as properties."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To turn our downloaded data into `DataLoaders` we need to tell fastai at least four things:\n",
+ "\n",
+ "- what kinds of data we are working with ;\n",
+ "- how to get the list of items ;\n",
+ "- how to label these items ;\n",
+ "- how to create the validation set.\n",
+ "\n",
+ "So far we have seen a number of *factory methods* for particular combinations of these things, which are convenient when you have an application and data structure which happens to fit into those predefined methods. For when you don't, fastai has an extremely flexible system called the *data block API*. With this API you can fully customize every stage of the creation of your DataLoaders. Here is what we need to create a DataLoaders for the dataset that we just downloaded:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bears = DataBlock(\n",
+ " blocks=(ImageBlock, CategoryBlock), \n",
+ " get_items=get_image_files, \n",
+ " splitter=RandomSplitter(valid_pct=0.3, seed=42),\n",
+ " get_y=parent_label,\n",
+ " item_tfms=Resize(128))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at each of these sections in turn:\n",
+ "\n",
+ "```python\n",
+ "blocks=(ImageBlock, CategoryBlock)\n",
+ "```\n",
+ "\n",
+ "This is a tuple where we specify what types we want for the *independent* and *dependent* variables. The *independent variable* is the thing we are using to make predictions from, and the *dependent variable* is our target. In this case, our independent variable is a set of images, and our dependent variable are the categories (type of bear) for each image. We will see many other types of block in the rest of this book.\n",
+ "\n",
+ "```python\n",
+ "get_items=get_image_files\n",
+ "```\n",
+ "\n",
+ "For this DataLoaders our underlying items will be file paths. We have to tell fastai how to get a list of those files. The `get_image_files` function takes a path, and returns a list of all of the images in that path (recursively, by default).\n",
+ "\n",
+ "```python\n",
+ "splitter=RandomSplitter(valid_pct=0.2, seed=42)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Often, datasets that you download will already have a validation set defined. Sometimes this is done by placing the images for the training and validation sets into different folders. Sometimes it is done by providing a CSV in which each file name is listed along with which dataset it should be in. There are many ways that this can be done, and fastai provides a very general approach which allows you to use one of fastai's predefined classes for this, or to write your own. In this case, however, we simply want to split our training and validation sets randomly. However, we would like to have the same training/validation split each time we run this notebook, so we fix the random seed. (Computers don't really know how to create random numbers at all, but simply create lists of numbers which look random. If you provide the same starting point for that list each time — called the *seed* — then you will get the exact same list each time.)\n",
+ "\n",
+ "```python\n",
+ "get_y=parent_label\n",
+ "```\n",
+ "\n",
+ "The independent variable is often referred to as \"x\" and the dependent variable is often referred to as \"y\". So in this section we are telling fastai what function to call to create the labels in our dataset. `parent_label` is a function provided by fastai which simply gets the name of the folder which a file is in. Because we put each of our bear images into folders based on the type of bear, this is going to give us the labels that we need.\n",
+ "\n",
+ "```python\n",
+ "item_tfms=Resize(128)\n",
+ "```\n",
+ "\n",
+ "Our images are all different sizes, and this is a problem for deep learning: we don't feed the model one image at a time but several (what we call a *mini-batch*) of them. To group them in a big array (usually called *tensor*) that is going to go through our model, they all need to be of the same size. So we need to add a transform twhich will resize these images to the same size. *item transforms* are pieces of code which run on each individual item, whether it be an image, category, or so forth. fastai includes many predefined transforms; we will use the `Resize` transform here.\n",
+ "\n",
+ "This command has given us a `DataBlock` object. This is like a *template* for creating a `DataLoaders`. We still need to tell fastai the actual source of our data — in this case, the path where the images can be found."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dls = bears.dataloaders(path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A DataLoaders includes validation and training `DataLoader`s. A `DataLoader` is a class which provides *batches* of a few items at a time to the GPU. We'll be learning a lot more about this class in the next chapter. When you loop through a `DataLoader` fastai will give you 64 (by default) items at a time, all stacked up into a single tensor. We can take a look at a few of those items by calling the `show_batch` method on a `DataLoader`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.594912 | \n", + "0.407416 | \n", + "0.823640 | \n", + "01:35 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.268259 | \n", + "0.316242 | \n", + "0.876000 | \n", + "03:03 | \n", + "
| 1 | \n", + "0.184861 | \n", + "0.246242 | \n", + "0.898080 | \n", + "03:10 | \n", + "
| 2 | \n", + "0.136392 | \n", + "0.220086 | \n", + "0.918200 | \n", + "03:16 | \n", + "
| 3 | \n", + "0.106423 | \n", + "0.191092 | \n", + "0.931360 | \n", + "03:15 | \n", + "
Learner.predict[source]
\n",
+ "\n",
+ "> Learner.predict(**`item`**, **`rm_type_tfms`**=*`None`*)\n",
+ "\n",
+ "Return the prediction on `item`, fully decoded, loss function decoded and probabilities\n",
+ "\n",
+ "Show in docs"
+ ],
+ "text/plain": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.359960 | \n", + "0.357917 | \n", + "0.831388 | \n", + "00:11 | \n", + "
| 1 | \n", + "0.353458 | \n", + "0.349657 | \n", + "0.837991 | \n", + "00:10 | \n", + "
| 2 | \n", + "0.338368 | \n", + "0.346997 | \n", + "0.843213 | \n", + "00:10 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "1.554056 | \n", + "1.428071 | \n", + "00:01 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "1.393103 | \n", + "1.361342 | \n", + "00:01 | \n", + "
| 1 | \n", + "1.297930 | \n", + "1.159169 | \n", + "00:00 | \n", + "
| 2 | \n", + "1.052705 | \n", + "0.827934 | \n", + "00:01 | \n", + "
| 3 | \n", + "0.810124 | \n", + "0.668735 | \n", + "00:01 | \n", + "
| 4 | \n", + "0.711552 | \n", + "0.627836 | \n", + "00:01 | \n", + "
| 5 | \n", + "0.657402 | \n", + "0.611715 | \n", + "00:01 | \n", + "
| 6 | \n", + "0.633079 | \n", + "0.605733 | \n", + "00:01 | \n", + "
| 7 | \n", + "0.622399 | \n", + "0.602674 | \n", + "00:01 | \n", + "
| 8 | \n", + "0.629075 | \n", + "0.601671 | \n", + "00:00 | \n", + "
| 9 | \n", + "0.619955 | \n", + "0.601550 | \n", + "00:01 | \n", + "
| \n", + " | userId | \n", + "movieId | \n", + "rating | \n", + "rating_pred | \n", + "
|---|---|---|---|---|
| 0 | \n", + "157 | \n", + "1200 | \n", + "4.0 | \n", + "3.558502 | \n", + "
| 1 | \n", + "23 | \n", + "344 | \n", + "2.0 | \n", + "2.700709 | \n", + "
| 2 | \n", + "19 | \n", + "1221 | \n", + "5.0 | \n", + "4.390801 | \n", + "
| 3 | \n", + "430 | \n", + "592 | \n", + "3.5 | \n", + "3.944848 | \n", + "
| 4 | \n", + "547 | \n", + "858 | \n", + "4.0 | \n", + "4.076881 | \n", + "
| 5 | \n", + "292 | \n", + "39 | \n", + "4.5 | \n", + "3.753513 | \n", + "
| 6 | \n", + "529 | \n", + "1265 | \n", + "4.0 | \n", + "3.349463 | \n", + "
| 7 | \n", + "19 | \n", + "231 | \n", + "3.0 | \n", + "2.881087 | \n", + "
| 8 | \n", + "475 | \n", + "4963 | \n", + "4.0 | \n", + "4.023387 | \n", + "
| 9 | \n", + "130 | \n", + "260 | \n", + "4.5 | \n", + "3.979703 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A random subset is a poor choice (too easy to fill in the gaps, and not indicative of what you'll need in production):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use the earlier data as your training set (and the later data for the validation set):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For example, Kaggle had a competition to [predict the sales in a chain of Ecuadorian grocery stores](https://www.kaggle.com/c/favorita-grocery-sales-forecasting). Kaggle's *training data* ran from Jan 1 2013 to Aug 15 2017 and the test data spanned Aug 16 2017 to Aug 31 2017. That way, the competition organizer ensured that entrants were making predictions for a time period that was *in the future*, from the perspective of their model. This is similar to the way quant hedge fund traders do *back-testing* to check whether their models are predictive of future periods, based on passed data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After time series, a second common case is when you can easily anticipate ways the data you will be making predictions for in production may be *qualitatively different* from the data you have to train your model with.\n",
+ "\n",
+ "In the Kaggle [distracted driver competition](https://www.kaggle.com/c/state-farm-distracted-driver-detection), the independent data are pictures of drivers at the wheel of a car, and the dependent variable is a category such as texting, eating, or safely looking ahead. If you were the insurance company building a model from this data, note that you would be most interested in how the model performs on drivers you haven't seen before (since you would likely have training data only for a small group of people). This is true of the Kaggle competition as well: the test data consists of people that weren't used in the training set."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you put one of the above images in your training set and one in the validation set, your model will seem to be performing better than it would on new people. Another perspective is that if you used all the people in training your model, your model may be overfitting to particularities of those specific people, and not just learning the states (texting, eating, etc).\n",
+ "\n",
+ "A similar dynamic was at work in the [Kaggle fisheries competition](https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring) to identify the species of fish caught by fishing boats in order to reduce illegal fishing of endangered populations. The test set consisted of boats that didn't appear in the training data. This means that you'd want your validation set to include boats that are not in the training set.\n",
+ "\n",
+ "Sometimes it may not be clear how your validation data will differ. For instance, for a problem using satellite imagery, you'd need to gather more information on whether the training set just contained certain geographic locations, or if it came from geographically scattered data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A _Choose Your Own Adventure_ moment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that you have got a taste of how to build a model, you can decide what you want to dig into next. If you would like to learn more about how to use deep learning models in practice, including identifying and fixing errors, and creating a real working web application, and how to avoid your model causing unexpected harm to your organization or society more generally, then keep reading the next chapters, _From model to production_, and _Data ethics_. If you would like to start learning the foundations of how deep learning works _under the hood_, skip to <"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Other data types**: Often you will find that domain-specific data types fit very nicely into existing categories. For instance, protein chains look a lot like natural language documents, in that they are long sequences of discrete tokens with complex relationships and meaning throughout the sequence. And indeed, it does turn out the using NLP deep learning methods is the current state of the art approach for many types of protein analysis. As another example: sounds can be represented as spectrograms, which can be treated as images; standard deep learning approaches for images turn out to work really well on spectrograms."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The Drivetrain approach"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are many accurate models that are of no use to anyone, and many inaccurate models that are highly useful. To ensure that your modeling work is useful in practice, you need to consider how your work will be used. In 2012 Jeremy, along with Margit Zwemer and Mike Loukides, introduced a method called *The Drivetrain Approach* for thinking about this issue, which we will summarize here. For more information, see the full article on oreilly.com [Designing Great Data Products](https://www.oreilly.com/radar/drivetrain-approach-data-products/).\n",
+ "\n",
+ "Consider a model in an autonomous vehicle, you want to help a car drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it doesn't stand on its own; as products become more sophisticated, it disappears into the plumbing. Someone using a self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach.\n",
+ "\n",
+ "We use data not just to generate more data (in the form of predictions), but to produce *actionable outcomes*. That is the goal of the Drivetrain Approach. Start by defining a clear **objective**. For instance, Google, when creating their first search engine, considered \"What is the user’s main objective in typing in a search query?\", and their answer was \"show the most relevant search result\". The next step is to consider what **levers** you can pull (i.e. what actions could you take) to better achieve that objective. In Google's case, that was the ranking of the search results. The third step was to consider what new **data** they would need to produce such a ranking; they realized that the implicit information regarding which pages linked to which other pages could be used for this purpose. Only after these first three steps do we begin thinking about building the predictive **models**. Our objective and available levers, what data we already have and what additional data we will need to collect, determine the models we can build. The models will take both the levers and any uncontrollable variables as their inputs; the outputs from the models can be combined to predict the final state for our objective."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's consider another example: recommendation systems. The **objective** of a recommendation engine is to drive additional sales by surprising and delighting the customer with recommendations of items they would not have purchased without the recommendation. The **lever** is the ranking of the recommendations. New **data** must be collected to generate recommendations that will *cause new sales*. This will require conducting many randomized experiments in order to collect data about a wide range of recommendations for a wide range of customers. This is a step that few organizations take; but without it, you don't have the information you need to actually optimize recommendations based on your true objective (more sales!)\n",
+ "\n",
+ "Finally, you could build two **models** for purchase probabilities, conditional on seeing or not seeing a recommendation. The difference between these two probabilities is a utility function for a given recommendation to a customer. It will be low in cases where the algorithm recommends a familiar book that the customer has already rejected (both components are small) or a book that he or she would have bought even without the recommendation (both components are large and cancel each other out).\n",
+ "\n",
+ "As you can see, in practice often the practical implementation of your model will require a lot more than just training a model! You'll often need to run experiments to collect more data, and consider how to incorporate your models into the overall system you're developing."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Starting your project"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So where should you start your deep learning journey? The most important thing is to ensure that you have some project that you are working on — it is only through working on your own projects that you will get real experience of building and using models. When selecting a project, the most important consideration is data availability. Regardless of whether you are doing a project just for your own learning, or for practical application in your organization, you want something where you can get started quickly. We have seen many students, researchers, and industry practitioners waste months or years while they attempt to find their perfect dataset. The goal is not to find the perfect dataset, or the perfect project, but just to get started, and iterate from there.\n",
+ "\n",
+ "If you take this approach, then you will be on your third iteration of learning and improving whilst the perfectionists are still in the planning stages!\n",
+ "\n",
+ "We also suggest that you iterate from end to end in your project; that is, don't spend months fine tuning your model, or polishing the perfect GUI, or labelling the perfect dataset… Instead, complete every step as well as you can in a reasonable amount of time, all the way to the end. For instance, if your final goal is an application that runs on a mobile phone, then that should be what you have after each iteration. But perhaps in the early iterations you take some shortcuts, for instance by doing all of the processing on a remote server, and using a simple responsive web application. By completing the project and to end, you will see where the most tricky bits are, and which bits make the biggest difference to the final result."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you work through this book, we suggest that you both complete lots of small experiments, by running and adjusting the notebooks we provide, at the same time that you gradually develop your own projects. That way, you will be getting experience with all of the tools and techniques that were explaining, as we discuss them.\n",
+ "\n",
+ "> s: To make the most of this book, take the time to experiment between each chapter, be it on your own project or exploring the notebooks we provide. Then try re-writing those notebooks from scratch on a new dataset. It's only by practicing (and failing) a lot that you will get an intuition on how to train a model. \n",
+ "\n",
+ "By using the end to end iteration approach you will also get a better understanding of how much data you really need. For instance, you may find you can only easily get 200 labelled data items, and you can't really know until you try whether that's enough to get the performance you need for your application to work well in practice.\n",
+ "\n",
+ "In an organizational context you will be able to show your colleagues that your idea can really work, by showing them a real working prototype. We have repeatedly observed that this is the secret to getting good organizational buy in for a project."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since it is easiest to get started on a project where you already have data available, that means it's probably easiest to get started on a project related to something you are already doing, because you already have data about things that you are doing. For instance, if you work in the music business, you may have access to many recordings. If you work as a radiologist, you probably have access to lots of medical images. If you are interested in wildlife preservation, you may have access to lots of images of wildlife.\n",
+ "\n",
+ "Sometimes, you have to get a bit creative. Maybe you can find some previous machine learning project, such as a Kaggle competition, that is related to your field of interest. Sometimes, you have to compromize. Maybe you can't find the exact data you need for the precise project you have in mind; but you might be able to find something from a similar domain, or measured in a different way, tackling a slightly different problem. Working on these kinds of similar projects will still give you a good understanding of the overall process, and may help you identify other shortcuts, data sources, and so forth.\n",
+ "\n",
+ "Especially when you are just starting out with deep learning it's not a good idea to branch out into very different areas to places that deep learning has not been applied to before. That's because if your model does not work at first, you will not know whether it is because you have made a mistake, or if the very problem you are trying to solve is simply not solvable with deep learning. And you won't know where to look to get help. Therefore, it is best at first to start with something where you can find an example online of somebody who has had good results with something that is at least somewhat similar to what you are trying to achieve, or where you can convert your data into a format similar what someone else has used before (such as creating an image from your data). Have a look at the *state of deep learning* earlier in this chapter for a reminder of what kinds of things deep learning is good at right now."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Gathering data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For many types of projects, you may be able to find all the data you need online. The project we'll be completing in this chapter is a *bear detector*. It will discriminate between three types of bear: grizzly, black, and teddy bear. There are many images on the internet of each type of bear we can use. We just need a way to find them and download them. We've provided a tool you can use for this purpose, so you can follow along with this chapter, creating your own image recognition application for whatever kinds of object you're interested in. In the fast.ai course, thousands of students have presented their work on the course forums, displaying everything from Trinidad hummingbird varieties, to Panama bus types, and even an application that helped one student let his fiancee recognize his sixteen cousins during Christmas vacation!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To download images, you should sign up at Microsoft for *Bing Image Search*. You will be given a key, which you can either paste over `os.environ('AZURE_SEARCH_KEY')` below, or you can set in your terminal as:\n",
+ "\n",
+ " export AZURE_SEARCH_KEY=your_key_here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "key = os.environ['AZURE_SEARCH_KEY']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As at the time of writing, Bing Image Search is the best option we know of for finding and downloading images. It's free for up to 1000 queries per month, and each query can download up to 150 images. However, something better might have come along between when we wrote this and when you're reading the book, so be sure to check out [book.fast.ai](https://book.fast.ai) where we'll let you know our current recommendation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "150"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results = search_images_bing(key, 'grizzly bear')\n",
+ "ims = results.attrgot('content_url')\n",
+ "len(ims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've successfully downloaded the URLs of 150 grizzly bears (or, at least, images that Bing Image Search finds for that search term). Let's look at one:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "ims = ['http://3.bp.blogspot.com/-S1scRCkI3vY/UHzV2kucsPI/AAAAAAAAA-k/YQ5UzHEm9Ss/s1600/Grizzly%2BBear%2BWildlife.jpg']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dest = 'images/grizzly.jpg'\n",
+ "download_url(ims[0], dest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So with this as your training data, you would end up not with a healthy skin detector, but a *young white woman touching her face* detector! Be sure to think carefully about the types of data that you might expect to see in practice in your application, and check carefully to ensure that all these types are reflected in your model's source data. (Thanks to Deb Raji, who came up with the *healthy skin* example. See her paper *Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products* for more fascinating insights into model bias.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From data to DataLoaders"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have downloaded and verified of the data that we want to use, we need to turn it into a `DataLoaders` object. `DataLoaders` is a thin class which just stores whatever `DataLoader` objects you pass to it, and makes them available as `train` and `valid` . Although it's a very simple class, it's very important in fastai: it provides the data for your model. The key functionality in `DataLoaders` is provided with just these 4 lines of code (it has some other minor functionality we'll skip over for now):\n",
+ "\n",
+ "```python\n",
+ "class DataLoaders(GetAttr):\n",
+ " def __init__(self, *loaders): self.loaders = loaders\n",
+ " def __getitem__(self, i): return self.loaders[i]\n",
+ " train,valid = add_props(lambda i,self: self[i])\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: DataLoaders: a fastai class which stores whatever `DataLoader` objects you pass to it, and makes them available as properties."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To turn our downloaded data into `DataLoaders` we need to tell fastai at least four things:\n",
+ "\n",
+ "- what kinds of data we are working with ;\n",
+ "- how to get the list of items ;\n",
+ "- how to label these items ;\n",
+ "- how to create the validation set.\n",
+ "\n",
+ "So far we have seen a number of *factory methods* for particular combinations of these things, which are convenient when you have an application and data structure which happens to fit into those predefined methods. For when you don't, fastai has an extremely flexible system called the *data block API*. With this API you can fully customize every stage of the creation of your DataLoaders. Here is what we need to create a DataLoaders for the dataset that we just downloaded:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bears = DataBlock(\n",
+ " blocks=(ImageBlock, CategoryBlock), \n",
+ " get_items=get_image_files, \n",
+ " splitter=RandomSplitter(valid_pct=0.3, seed=42),\n",
+ " get_y=parent_label,\n",
+ " item_tfms=Resize(128))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at each of these sections in turn:\n",
+ "\n",
+ "```python\n",
+ "blocks=(ImageBlock, CategoryBlock)\n",
+ "```\n",
+ "\n",
+ "This is a tuple where we specify what types we want for the *independent* and *dependent* variables. The *independent variable* is the thing we are using to make predictions from, and the *dependent variable* is our target. In this case, our independent variable is a set of images, and our dependent variable are the categories (type of bear) for each image. We will see many other types of block in the rest of this book.\n",
+ "\n",
+ "```python\n",
+ "get_items=get_image_files\n",
+ "```\n",
+ "\n",
+ "For this DataLoaders our underlying items will be file paths. We have to tell fastai how to get a list of those files. The `get_image_files` function takes a path, and returns a list of all of the images in that path (recursively, by default).\n",
+ "\n",
+ "```python\n",
+ "splitter=RandomSplitter(valid_pct=0.2, seed=42)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Often, datasets that you download will already have a validation set defined. Sometimes this is done by placing the images for the training and validation sets into different folders. Sometimes it is done by providing a CSV in which each file name is listed along with which dataset it should be in. There are many ways that this can be done, and fastai provides a very general approach which allows you to use one of fastai's predefined classes for this, or to write your own. In this case, however, we simply want to split our training and validation sets randomly. However, we would like to have the same training/validation split each time we run this notebook, so we fix the random seed. (Computers don't really know how to create random numbers at all, but simply create lists of numbers which look random. If you provide the same starting point for that list each time — called the *seed* — then you will get the exact same list each time.)\n",
+ "\n",
+ "```python\n",
+ "get_y=parent_label\n",
+ "```\n",
+ "\n",
+ "The independent variable is often referred to as \"x\" and the dependent variable is often referred to as \"y\". So in this section we are telling fastai what function to call to create the labels in our dataset. `parent_label` is a function provided by fastai which simply gets the name of the folder which a file is in. Because we put each of our bear images into folders based on the type of bear, this is going to give us the labels that we need.\n",
+ "\n",
+ "```python\n",
+ "item_tfms=Resize(128)\n",
+ "```\n",
+ "\n",
+ "Our images are all different sizes, and this is a problem for deep learning: we don't feed the model one image at a time but several (what we call a *mini-batch*) of them. To group them in a big array (usually called *tensor*) that is going to go through our model, they all need to be of the same size. So we need to add a transform twhich will resize these images to the same size. *item transforms* are pieces of code which run on each individual item, whether it be an image, category, or so forth. fastai includes many predefined transforms; we will use the `Resize` transform here.\n",
+ "\n",
+ "This command has given us a `DataBlock` object. This is like a *template* for creating a `DataLoaders`. We still need to tell fastai the actual source of our data — in this case, the path where the images can be found."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dls = bears.dataloaders(path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A DataLoaders includes validation and training `DataLoader`s. A `DataLoader` is a class which provides *batches* of a few items at a time to the GPU. We'll be learning a lot more about this class in the next chapter. When you loop through a `DataLoader` fastai will give you 64 (by default) items at a time, all stacked up into a single tensor. We can take a look at a few of those items by calling the `show_batch` method on a `DataLoader`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls.valid.show_batch(max_n=4, rows=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By default `Resize` *crops* the images to fit a square shape of the size requested, using the full width or height. This can result in losing some important details. Alternatively, you can ask fastai to pad the images with zeros (which is black), or squish/stretch them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bears = bears.new(item_tfms=Resize(128, ResizeMethod.Squish))\n",
+ "dls = bears.dataloaders(path)\n",
+ "dls.valid.show_batch(max_n=4, rows=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bears = bears.new(item_tfms=Resize(128, ResizeMethod.Pad, pad_mode='zeros'))\n",
+ "dls = bears.dataloaders(path)\n",
+ "dls.valid.show_batch(max_n=4, rows=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data augmentation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "All of these approaches seem somewhat wasteful, or problematic. If we squished or stretch the images then the end up unrealistic shapes, leading to a model that learns that things look different to how they actually are, which we would expect to result in lower accuracy. If we crop the images then we remove some of the features that allow us to recognize them. For instance, if we were trying to recognise the breed of dog or cat, we may end up cropping out a key part of the body or the face necessary to distinguish between similar breeds. If we pat the images then we have a whole lot of empty space, which is just wasted computation for our model, and results in a lower effective resolution for the part of the image we actually use.\n",
+ "\n",
+ "Instead, what we normally do in practice is to randomly select part of the image, and crop to just that part. On each epoch (which is one complete pass through all of our images in the dataset) we randomly select a different part of each image. This means that our model can learn to focus on, and recognize, different features in our images. It also reflects how images work in the real world; different photos of the same thing may be framed in slightly different ways.\n",
+ "\n",
+ "Here is a another copy of the previous examples, but this time we are replacing `Resize` with `RandomResizedCrop`, which is the transform that provides the behaviour described above.The most important parameter to pass in is the `min_scale` parameter, which determines how much of the image to select at minimum each time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bears = bears.new(item_tfms=RandomResizedCrop(128, min_scale=0.3))\n",
+ "dls = bears.dataloaders(path)\n",
+ "dls.train.get_idxs = lambda: Inf.ones\n",
+ "dls.train.show_batch(max_n=4, rows=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: The second line in this code is a little bit magic, and you absolutely don't have to understand it at this point. So feel free to ignore the entirety of this paragraph! This is for just if you're curious… Showing different randomly varied versions of the same image is not something we normally have to do in deep learning, so it's not something that fastai provides directly. Therefore to draw the picture of data augmentation on the same image, we had to take advantage of fastai's sophisticated customisation features. DataLoader has a method called `get_idx`, which is called to decide which items should be selected next. Normally when we are training, this returns a random permutation of all of the indexes in the dataset. But pretty much everything in fastai can be changed, including how the `get_idx` method is defined, which means we can change how we sample data. So in this case, we are replacing it with a version which always returns the number one. That way, our DataLoader shows the same image again and again! This is a great example of the flexibility that fastai provides. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In fact, an entirely untrained neural network knows nothing whatsoever about how images behave. It doesn't even recognise that when an object is moved one pixel to the left, then it still is a picture of the same thing! So actually training the neural network with examples of images that are in slightly different places, and slightly different sizes, helps it to understand the basic concept of what a *object* is, and how it can be represented in an image.\n",
+ "\n",
+ "This is a specific example of a more general technique, called *data augmentation*. Data augmentation refers to creating random variations of our input data, such that they appear a different, but are not expected to change the meaning of the data. Examples of common data augmentation for images are rotation, flipping, perspective warping, brightness changes, contrast changes, and much more. For natural photo images such as the ones we are using here, there is a standard set of augmentations which we have found work pretty well, and are provided with the get transforms function. Because the images are now all the same size, we can apply these augmentation is to an entire batch of the time using the GPU, which will save a lot of time. To tell fastai we want to use these transforms to a batch, we use the `batch_tfms` parameter. (Note that's we're not using `RandomResizedCrop` in this example, so you can see the differences more clearly; we're also using double the amount of augmentation compared to the default, for the same reason)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "bears = bears.new(item_tfms=Resize(128), batch_tfms=aug_transforms(mult=2))\n",
+ "dls = bears.dataloaders(path)\n",
+ "dls.train.get_idxs = lambda: Inf.ones\n",
+ "dls.train.show_batch(max_n=8, rows=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Training your model, and using it to clean your data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll use `RandomResizedCrop` and default `aug_transforms` for our model, and an image size of 224px, which is fairly standard for image classification."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bears = bears.new(\n",
+ " item_tfms=RandomResizedCrop(224, min_scale=0.5),\n",
+ " batch_tfms=aug_transforms())\n",
+ "dls = bears.dataloaders(path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create our `Learner` and fine tune it in the usual way."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = cnn_learner(dls, resnet18, metrics=error_rate)\n",
+ "learn.fine_tune(4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's see whether the mistakes the model is making is mainly thinking that grizzlies are teddies (that would be bad for safety!), or that grizzlies are black bears, or something else. We can create a *confusion matrix*:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.235733 | \n", + "0.212541 | \n", + "0.087302 | \n", + "00:05 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.213371 | \n", + "0.112450 | \n", + "0.023810 | \n", + "00:05 | \n", + "
| 1 | \n", + "0.173855 | \n", + "0.072306 | \n", + "0.023810 | \n", + "00:06 | \n", + "
| 2 | \n", + "0.147096 | \n", + "0.039068 | \n", + "0.015873 | \n", + "00:06 | \n", + "
| 3 | \n", + "0.123984 | \n", + "0.026801 | \n", + "0.015873 | \n", + "00:06 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interp = ClassificationInterpretation.from_learner(learn)\n",
+ "interp.plot_confusion_matrix()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each row here represents all the black, grizzly, and teddy bears in our dataset, respectively. Each column represents the images which the model predicted as black, grizzly, and teddy bears, respectively. Therefore, the diagonal of the matrix shows the images which were classified correctly, and the other, off diagonal, cells represent those which were classified incorrectly. This is called a *confusion matrix* and is one of the many ways that fastai allows you to view the results of your model. It is (of course!) calculated using the validation set. With the color coding, the goal is to have white everywhere, except the diagonal where we want dark blue. Our bear classifier isn't making many mistakes!\n",
+ "\n",
+ "It's helpful to see where exactly our errors are occuring, to see whether it's due to a dataset problem (e.g. images that aren't bears at all, or are labelled incorrectly, etc), or a model problem (e.g. perhaps it isn't handling images taken with unusual lighting, or from a different angle, etc.) To do this, we can sort out images by their *loss*. The *loss* is a number that is higher if the model is incorrect (and especially if it's also confident of its incorrect answer), or if it's correct, but not confident of its correct answer. (We'll learn how loss is calculated later in the book.) `plot_top_losses` shows us the images with the highest loss in our dataset. As the title of the output says, each image is labeled with four things: prediction, actual (target label), loss, and probability. The *probability* here is the confidence level, from zero to one, that the model has assigned to its prediction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interp.plot_top_losses(5, rows=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This output shows that the highest loss is an image that has been predicted as \"grizzly\" with high confidence. However, it's labeled (based on our Bing image search) as \"black\". We're not bear experts, but it sure looks to us like this label is incorrect! We should probably change its label to \"grizzly\".\n",
+ "\n",
+ "The intuitive approach to doing data cleaning is to do it *before* you train a model. But as you've seen in this case, a model can actually help you find data issues more quickly and easily. So we normally prefer to train a quick and simple model first, and then use it to help us with data cleaning.\n",
+ "\n",
+ "fastai includes a handy GUI for data cleaning called `ImageClassifierCleaner`, which allows you to choose a category, and training vs validation set, and view the highest-loss images (in order), along with menus to allow any images to be selected for removal, or relabeling."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d547f14e0f7848f39627ebb88d457e64",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "VBox(children=(Dropdown(options=('black', 'grizzly', 'teddy'), value='black'), Dropdown(options=('Train', 'Val…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "cleaner = ImageClassifierCleaner(learn)\n",
+ "cleaner"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Cleaner](\"images/att_00007.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that amongst our *black bears* is an image that contain two bears, one grizzly, one black. So we should choose `` in the menu under this image. `ImageClassifierCleaner` doesn't actually do the deleting or changing of labels for you; it just returns the indices of items to change. So, for instance, to delete (`unlink`) all images selected for deletion, we would run:\n",
+ "\n",
+ "```python\n",
+ "for idx in cleaner.delete(): cleaner.fns[idx].unlink()\n",
+ "```\n",
+ "\n",
+ "To move images where we've selected a different category, we would run:\n",
+ "\n",
+ "```python\n",
+ "for idx,cat in cleaner.change(): shutil.move(cleaner.fns[idx], path/cat)\n",
+ "```\n",
+ "\n",
+ "> s: Cleaning the data or getting it ready for your model are two of the biggest challenges for data scientists, one they say take 90% of their time. The fastai library aims at providing tools to make it as easy as possible.\n",
+ "\n",
+ "We'll be seeing more examples of model-driven data cleaning throughout this book. Once we've cleaned up our data, we can retrain our model. Try it yourself, and see if your accuracy improves!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: After cleaning the dataset using the above steps, we generally are seeing 100% accuracy on this task. We even see that result when we download a lot less images than the 150 per class we're using here. As you can see, the common complaint *you need massive amounts of data to do deep learning* can be a very long way from the truth!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Turning your model into an online application"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Using the model for inference"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are now going to look at what it takes to take this model and turn it into a working online application. We will just go as far as creating a basic working prototype; we do not have the scope in this book to teach you all the details of web application development generally.\n",
+ "\n",
+ "Once you've got a model you're happy with, you need to save it, so that you can then copy it over to a server where you'll use it in production. Do you remember exactly what a model is? It consists of two parts: the *architecture*, and the trained *parameters*. The easiest way to save a model is to save both of these, because that way when you load a model you can be sure that you have the matching architecture and parameters. To save both parts, use the `export` method.\n",
+ "\n",
+ "This method even saves the definition of how to create your `DataLoaders`. This is important, because otherwise you would have to redefine how to transform your data in order to use your model in production. When you call export, fastai will save a file called `export.pkl`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.export()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's check that file exists:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#1) [Path('export.pkl')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path = Path()\n",
+ "Path().ls(file_exts='.pkl')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You'll need this file wherever you deploy your app to. For now, let's try to create a simple app within our notebook.\n",
+ "\n",
+ "When we use a model for getting predictions, instead of training, we call it *inference*. To create our inference learner from the exported file, we use `load_learner` (in this case, this isn't really necessary, since we already have a working `Learner` in our notebook; we're just doing it here so you can see the whole process end-to-end):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn_inf = load_learner(path/'export.pkl')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we're doing inference, we're generally just getting predicitions for one image at a time. To do this, pass a filename to `predict`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "('grizzly', tensor(1), tensor([9.0767e-06, 9.9999e-01, 1.5748e-07]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn_inf.predict('images/grizzly.jpg')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This has returned three things: the predicted category in the same format you originally provided, in this case that's a string), the index of the predicted category, and the probabilities of each category. The last two are based on the order of categories in the *vocab* of the `DataLoaders`; that is, the stored list of all possible categories. At inference time, you can access the `DataLoaders` as an attribute of the `Learner`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#3) ['black','grizzly','teddy']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn_inf.dls.vocab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see here that if we index into the vocab with the integer returned by `predict` then we get back \"grizzly\", as expected. Also, note that if we index into the list of probabilities, we see a nearly 1.00 probability that this is a grizzly."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating a Notebook app from the model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To use our model in an application we can simply treat the `predict` method as a regular function. Therefore, creating an app from the model can be done using any of the myriad of frameworks and techniques available to application developers.\n",
+ "\n",
+ "However, most data scientists are not familiar with the world of web application development. So let's try using something that you do, at this point, know: Jupyter notebooks. It turns out that we can create a complete working web application using nothing but Jupyter notebooks! The two things we need to make this happen are:\n",
+ "\n",
+ "- IPython widgets (ipywidgets)\n",
+ "- Voilà\n",
+ "\n",
+ "*IPython widgets* are GUI components that bring together JavaScript and Python functionality in a web browser, and can be created and used within a Jupyter notebook. For instance, the image cleaner that we saw earlier in this chapter is entirely written with IPython widgets. However, we don't want to require users of our application to have to run Jupyter themselves.\n",
+ "\n",
+ "That is why *Voilà* exists. It is a system for making applications consisting of IPython widgets available to end-users, without them having to use Jupyter at all. Voila is taking advantage of the fact that a notebook _already is_ a kind of web application, just a rather complex one that depends on another web application Jupyter itself. Essentially, it helps us automatically convert the complex web application which we've already implicitly made (the notebook) into a simpler, easier-to-deploy web application, which functions like a normal web application rather than like a notebook.\n",
+ "\n",
+ "But we still have the advantage of developing in a notebook. So with ipywidgets, we can build up our GUI step by step. We will use this approach to create a simple image classifier. First, we need a file upload widget:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e0c4141e3c76425c98ae9994ccf9a748",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "FileUpload(value={}, description='Upload')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "btn_upload = widgets.FileUpload()\n",
+ "btn_upload"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "Now we can grab the image:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "# For the book, we can't actually click an upload button, so we fake it\n",
+ "btn_upload = SimpleNamespace(data = ['images/grizzly.jpg'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = PILImage.create(btn_upload.data[-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Output](\"images/att_00009.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use an `Output` widget to display it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide_output\n",
+ "out_pl = widgets.Output()\n",
+ "out_pl.clear_output()\n",
+ "with out_pl: display(img.to_thumb(128,128))\n",
+ "out_pl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "Then we can get our predictions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pred,pred_idx,probs = learn_inf.predict(img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and use a `Label` to display them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "08509e39d3454701b5fed10439970e84",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Label(value='Prediction: grizzly; Probability: 1.0000')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "lbl_pred = widgets.Label()\n",
+ "lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
+ "lbl_pred"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`Prediction: grizzly; Probability: 1.0000`\n",
+ "\n",
+ "We'll need a button to do the classification, it looks exactly like the upload button."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5948c2dc026d43cb9afdce7dee8fa425",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Button(description='Classify', style=ButtonStyle())"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "btn_run = widgets.Button(description='Classify')\n",
+ "btn_run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and a *click event handler*, that is, a function that will be called when it's pressed; we can just copy over the lines of code from above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def on_click_classify(change):\n",
+ " img = PILImage.create(btn_upload.data[-1])\n",
+ " out_pl.clear_output()\n",
+ " with out_pl: display(img.to_thumb(128,128))\n",
+ " pred,pred_idx,probs = learn_inf.predict(img)\n",
+ " lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
+ "\n",
+ "btn_run.on_click(on_click_classify)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can test the button now by pressing it, and you should see the image and predictions above update automatically!\n",
+ "\n",
+ "We can now put them all in a vertical box (`VBox`) to complete our GUI:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "#Putting back btn_upload to a widget for next cell\n",
+ "btn_upload = widgets.FileUpload()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e9e7b05555a44125ac0e5365e17ea59d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "VBox(children=(Label(value='Select your bear!'), FileUpload(value={}, description='Upload'), Button(descriptio…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "VBox([widgets.Label('Select your bear!'), btn_upload, btn_run, out_pl, lbl_pred])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"The](\"images/att_00011.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Turning your notebook into a real app"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have everything working in this Jupyter notebook, we can create our application. To do this, create a notebook which contains only the code needed to create and show the widgets that you need, and markdown for any text that you want to appear. Have a look at the *bear_classifier* notebook in the book repo to see the simple notebook application we created.\n",
+ "\n",
+ "Next, install Voila if you have not already, by copying these lines into a Notebook cell, and executing it:\n",
+ "\n",
+ " !pip install voila\n",
+ " !jupyter serverextension enable voila --sys-prefix\n",
+ "\n",
+ "Cells which begin with a `!` do not contain Python code, but instead contain code which is passed to your shell, such as bash, power shell in windows, or so forth. If you are comfortable using the command line (which we'll be learning about later in this book), you can of course simply type these two lines (without the `!` prefix) directly into your terminal. In this case, the first line installs the voila library and application, and the second connects it to your existing Jupyter notebook.\n",
+ "\n",
+ "Voila runs Jupyter notebooks, just like the Jupyter notebook server you are using now does, except that it does something very important: it removes all of the cell inputs, and only shows output (including ipywidgets), along with your markdown cells. So what's left is a web application! To view your notebook as a voila web application replace the word \"notebooks\" in your browser's URL with: \"voila/render\". You will see the same content as your notebook, but without any of the code cells.\n",
+ "\n",
+ "Of course, you don't need to use Voila or ipywidgets. Your model is just a function you can call: `pred,pred_idx,probs = learn.predict(img)` . So you can use it with any framework, hosted on any platform. And you can take something you've prototyped in ipywidgets and Voila and later convert it into a regular web application. We're showing you this approach in the book because we think it's a great way for data scientists and other folks that aren't web development experts to create applications from their models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Deploying your app"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we now know, you need a GPU to train nearly any useful deep learning model. So, do you need a GPU to use that model in production? No! You almost certainly **do not need a GPU to serve your model in production**. There's a few reasons for this:\n",
+ "\n",
+ "- As we've seen, GPUs are only useful when they do lots of identical work in parallel. If you're doing (say) image classification, then you'll normally be classifying just one user's image at a time, and there isn't normally enough work to do in a single image to keep a GPU busy for long enough for it to be very efficient. So a CPU will often be more cost effective.\n",
+ "- An alternative could be to wait for a few users to submit their images, and then batch them up, and do them all at once on a GPU. But then you're asking your users to wait, rather than getting answers straight away! And you need a high volume site for this to be workable.\n",
+ "- The complexities of dealing with GPU inference are significant. In particular, the GPU's memory will need careful manual management, and you'll need some careful queueing system to ensure you only do one batch at a time\n",
+ "- There's a lot more market competition in CPU servers than GPU, as a result of which there's much cheaper options available for CPU servers.\n",
+ "\n",
+ "Because of the complexity of GPU serving, many systems have sprung up to try to automate this. However, managing and running these systems is themselves complex, and generally requires compiling your model into a different form that's specialized for that system. It doesn't make sense to deal with this complexity until/unless your app gets popular enough that it makes clear financial sense for you to do so."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For at least the initial prototype of your application, and for any hobby projects that you want to show off, you can easily host them for free. The best place and the best way to do this will vary over time so check the book website for the most up-to-date recommendations. As we're writing this book in 2020 the simplest (and free!) approach is called [Binder](https://mybinder.org/). To publish your web app on Binder, you follow these steps:\n",
+ "\n",
+ "1. Add your notebook to a [GitHub repository](http://github.com/), \n",
+ "2. Paste the URL of that repo in the URL field of Binder, \n",
+ "3. Change the \"File\" dropdown to instead select \"URL\",\n",
+ "4. In the Path field, enter `/voila/render/name.ipynb` (replacing `name.ipynb` as appropriate for your notebook):\n",
+ "5. Click the \"Copy the URL\" button and paste it somewhere safe. \n",
+ "6. Click \"Launch\"."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Deploying](\"images/att_00001.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first time you do this Binder will take around 5 minutes to build your site. In other words, is it finding a virtual machine which can run your app, allocating storage, collecting the files needed for Jupyter, for your notebook, and for presenting your notebook as a web application. It's doing all of this behind the scenes.\n",
+ "\n",
+ "Finally, once it has started the app running, it will navigate your browser to your new web app. You can share the URL you copied to allow others to access your app as well.\n",
+ "\n",
+ "For other (both free and paid) options for deploying your web app, be sure to take a look at the book web site."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You may well want to deploy your application onto mobile devices, or edge devices such as a Raspberry Pi. There are a lot of libraries and frameworks to allow you to integrate a model directly into a mobile application. However these approaches tend to require a lot of extra steps and boilerplate, and do not always support all the PyTorch and fastai layers that your model might use. In addition, the work you do will depend on what kind of mobile devices you are targeting for deployment. So you might need to do some work to run on iOS devices, different work to run on newer Android devices, different work for older Android devices, etc.. Instead, we recommend wherever possible that you deploy the model itself to a server, and have your mobile or edge application connect to it as a web service.\n",
+ "\n",
+ "There is quite a few upsides to this approach. The initial installation is easier, because you only have to deploy a small GUI application, which connects to the server to do all the heavy lifting. More importantly perhaps, upgrades of that core logic can happen on your server, rather than needing to be distributed to all of your users. Your server can have a lot more memory and processing capacity than most edge devices, and it is far easier to scale those resources if your model becomes more demanding. The hardware that you will have on a server is going to be more standard and more easily supported by fastai and PyTorch, so you don't have to compile your model into a different form.\n",
+ "\n",
+ "There are downsides too, of course. Your application will require a network connection, and there will be some latency each time the model is called. It takes a while for a neural network model to run anyway, so this additional network latency may not make a big difference to your users in practice. In fact, since you can use better hardware on the server, the overall latency may even be less! If your application uses sensitive data then your users may be concerned about an approach which sends that data to a remote server, so sometimes privacy considerations will mean that you need to run the model on the edge device. Sometimes this can be avoided by having a *on premise* server, such as inside a company's firewall. Managing the complexity and scaling the server can create additional overhead, whereas if your model runs on the edge devices then each user is bringing their own compute resources, which leads to easier scaling with an increasing number of users (also known as _horizontal scaling_)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> A: I've had a chance to see up close how the mobile ML landscape is changing in my work. We offer an iPhone app that depends on computer vision and for years we ran our own computer vision models in the cloud. This was the only way to do it then since those models needed significant memory and compute resources and took minutes to process. This approach required building not only the models (fun!) but infrastructure to ensure a certain number of \"compute worker machines\" was absolutely always running (scary), that more machines would automatically come online if traffic increased, that there was stable storage for large inputs and outputs, that the iOS app could know and tell the user how their job was doing, etc... Nowadays, Apple provides APIs for converting models to run efficiently on device and most iOS devices have dedicated ML hardware, so we run our new models on device. So, in a few years that strategy has gone from impossible to possible but it's still not easy. In our case it's worth it, for a faster user experiene and to worry less about servers. What works for you will depend, realistically, on the user experience you're trying to create and what you personally find it easy to do. If you really know how to run servers, do it. If you really know how to build native mobile apps, do that. There are many roads up the hill.\n",
+ "\n",
+ "Overall, we'd recommend using a simple CPU-based server approach where possible, for as long as you can get away with it. If you're lucky enough to have a very successful application, then you'll be able to justify the investment in more complex deployment approaches at that time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How to avoid disaster"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In practice, a deep learning model will be just one piece of a much bigger system. As we discussed at the start of this chapter, a *data product* requires thinking about the entire end to end process within which or model lives.\n",
+ "\n",
+ "One of the biggest issues with this is that understanding and testing the behavior of a deep learning model is much more difficult than most code that you would write. With normal software development you can analyse the exact steps that the software is taking, and carefully study with of these steps match the desired behaviour that you are trying to create. But with a neural network the behavior emerges from the models attempt to match the training data, rather than being exactly defined.\n",
+ "\n",
+ "This can result in disaster! For instance, let's say you really were rolling out a bear detection system which will be attached to video cameras around the campsite, and will warn campers of incoming bears. If we used a model trained with the dataset we downloaded, there are going to be all kinds of problems in practice, such as:\n",
+ "\n",
+ "- working with video data instead of images ;\n",
+ "- handling nighttime images, which may not appear in this dataset ;\n",
+ "- dealing with low resolution camera images ;\n",
+ "- ensuring results are returned fast enough to be useful in practice ;\n",
+ "- recognising bears in positions that are rarely seen in photos that people post online (for example from behind, partially covered by bushes, or when a long way away from the camera)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A big part of the issue is that the kinds of photos that people are most likely to upload to the Internet are the kinds of photos that do a good job of clearly and artistically displaying their subject matter. So we may need to do a lot of our own data collection and labelling to create a useful system.\n",
+ "\n",
+ "This is just one example of the more general problem of *out of domain* data. That is to say, there may be data that our model sees in production which is very different to what it saw during training. There isn't really a complete technical solution to this problem; instead we have to be careful about our approach to rolling out the technology.\n",
+ "\n",
+ "There are other reasons we need to be careful too. One very common problem is *domain shift*; this is where the type of data that our model sees changes over time. For instance, an insurance company may use a deep learning model as part of their pricing and risk algorithm, but over time the type of customers that they attract, and the type of risks that they represent, may change so much that the original training data is no longer relevant.\n",
+ "\n",
+ "Out of domain data, and domain shift, are examples of the problem that you can never fully no the entire behaviour of your neural network. They have far too many parameters to be able to analytically understand all of their possible behaviours. This is the natural downside of the thing that they're so good at — their flexibility in being able to solve complex problems where we may not even be able to fully specify our preferred solution approaches. The good news, however, is that there are ways to mitigate these risks using a carefully thought out process. The details of this will vary depending on the details of the problem you are solving, but we will attempt to lay out here a high-level approach which we hope will provide useful guidance."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Deployment](\"images/att_00061.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Where possible, the first step is to use an entirely manual process, with your deep learning model approach running in parallel, but not being used directly to drive any actions. The humans involved in the manual process should look at the deep learning outputs and check whether they make sense. For instance, with our bear classifier a park ranger could have a screen displaying any time a possible bear sighting occurred in any camera, and simply highlight them in red on the screen. The park ranger would still be expected to be just as alert as before the model was deployed; they are simply helping to check for problems at this point.\n",
+ "\n",
+ "The second step is to try to limit the scope of the model, and have it carefully supervised by people. For instance, do a small geographically and time constrained trial of the model-driven approach. Rather than rolling your bear classifier out in every national park throughout the country, pick a single observation post, for a one-week period, and have a park ranger check each alert before it goes out.\n",
+ "\n",
+ "Then, gradually increase the scope of your rollout. As you do so, ensure that you have really good reporting systems in place, to make sure that you are aware of any significant changes to the actions being taken compared to your manual process. For instance, if the number of bear alerts doubles or halves after rollout of the new system in some location we should be very concerned. Try to think about all the ways in which your system could go wrong, and then think about what measure or report or picture could reflect that problem, and then ensure that your regular reporting includes that information."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: I started a company 20 years ago called *Optimal Decisions* which used machine learning and optimisation to help giant insurance companies set their pricing, impacting tens of billions of dollars of risks. We used the approaches described above to manage the potential downsides of something that might go wrong. Also, before we worked with our clients to put anything in production, we tried to simulate the impact by testing the end to end system on their previous year's data. It was always quite a nerve-wracking process, putting these new algorithms in production, but every rollout was successful."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Unforeseen consequences and feedback loops"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the biggest challenges in rolling out a model is that your model may change the behaviour of the system it is a part of. For instance, consider YouTube's recommendation system. A couple of years ago Google talked about how they had introduced reinforcement learning (closely related to deep learning, but where your loss function represents a result which could be a long time after an action occurs) to improve their recommendation system. They described how they used an algorithm which made recommendations such that watch time would be optimised.\n",
+ "\n",
+ "However, human beings tend to be drawn towards controversial content. This meant that videos about wings like conspiracy theories started to get recommended more and more by the recommendation system. Furthermore, it turns out that the kinds of people that are interested in conspiracy theories are also people that watch a lot of online videos! So, they started to get drawn more and more towards YouTube. The increasing number of conspiracy theorists watching YouTube resulted in the algorithm recommending more and more conspiracy theories and other extremist content, which resulted in more extremists watching videos on YouTube, and more people watching YouTube developing extremist views, which led to the algorithm recommending more extremist content... The system became so out of control that in February 2019 it led the New York Times to run the headline \"YouTube Unleashed a Conspiracy Theory Boom. Can It Be Contained?\"\n",
+ "\n",
+ "A helpful exercise prior to rolling out a significant machine learning system is to consider this question: \"what would happen if it went really, really well?\" In other words, what if the predictive power was extremely high, and its ability to influence behaviour was extremely significant? In that case, who would be most impacted? What would the most extreme results potentially look like? How would you know what was really going on?\n",
+ "\n",
+ "Such a thought exercise might help you to construct a more careful rollout plan, ongoing monitoring systems, and human oversight. Of course, human oversight isn't useful if it isn't listened to; so make sure that there are reliable and resilient communication channels so that the right people will be aware of issues, and will have the power to fix them."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get writing!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the things our students have found most helpful to solidify their understanding of this material is to write it down. There is no better test of your understanding of a topic than attempting to teach it to somebody else. This is helpful even if you never show your writing to anybody — but it's even better if you share it! So we recommend that, if you haven't already, you start a blog. Now that you've finished chapter 2, and have learned how to train and deploy models, you're well placed to write your first blog post about your deep learning journey. What's surprised you? What opportunities do you see for deep learning in your field? What obstacles do you see?\n",
+ "\n",
+ "Rachel Thomas, co-founder of fast.ai, wrote in the article [Why you (yes, you) should blog](https://medium.com/@racheltho/why-you-yes-you-should-blog-7d2544ac1045):\n",
+ "\n",
+ "```asciidoc\n",
+ "____\n",
+ "The top advice I would give my younger self would be to start blogging sooner. Here are some reasons to blog:\n",
+ "\n",
+ "* It’s like a resume, only better. I know of a few people who have had blog posts lead to job offers!\n",
+ "* Helps you learn. Organizing knowledge always helps me synthesize my own ideas. One of the tests of whether you understand something is whether you can explain it to someone else. A blog post is a great way to do that.\n",
+ "* I’ve gotten invitations to conferences and invitations to speak from my blog posts. I was invited to the TensorFlow Dev Summit (which was awesome!) for writing a blog post about how I don’t like TensorFlow.\n",
+ "* Meet new people. I’ve met several people who have responded to blog posts I wrote.\n",
+ "* Saves time. Any time you answer a question multiple times through email, you should turn it into a blog post, which makes it easier for you to share the next time someone asks.\n",
+ "____\n",
+ "```\n",
+ "\n",
+ "Perhaps her most important tip is this: \"*You are best positioned to help people one step behind you. The material is still fresh in your mind. Many experts have forgotten what it was like to be a beginner (or an intermediate) and have forgotten why the topic is hard to understand when you first hear it. The context of your particular background, your particular style, and your knowledge level will give a different twist to what you’re writing about*.\"\n",
+ "\n",
+ "We've provided full details on how to set up a blog in an appendix \"_Creating a blog_\". If you don't have a blog already, jump over to that chapter now, because we've got a really great approach set up for you to start blogging, for free, with no ads--and you can even use Jupyter Notebook!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Provide an example of where the bear classification model might work poorly, due to structural or style differences to the training data\n",
+ "1. Where do text models currently have a major deficiency?\n",
+ "1. What are possible negative societal implications of text generation models?\n",
+ "1. In situations where a model might make mistakes, and those mistakes could be harmful, what is a good alternative to automating a process?\n",
+ "1. What kind of tabular data is deep learning particularly good at?\n",
+ "1. What's a key downside of directly using a deep learning model for recommendation systems?\n",
+ "1. What are the steps of the Drivetrain approach?\n",
+ "1. How do the steps of the Drivetrain approach map to a recommendation system?\n",
+ "1. Create an image recognition model using data you curate, and deploy it on the web.\n",
+ "1. What is `DataLoaders`?\n",
+ "1. What four things do we need to tell fastai to create `DataLoaders`?\n",
+ "1. What does the `splitter` parameter to `DataBlock` do?\n",
+ "1. How do we ensure a random split always gives the same validation set?\n",
+ "1. What letters are often used to signify the independent and dependent variables?\n",
+ "1. What's the difference between crop, pad, and squish resize approaches? When might you choose one over the other?\n",
+ "1. What is data augmentation? Why is it needed?\n",
+ "1. What is the difference between `item_tfms` and `batch_tfms`?\n",
+ "1. What is a confusion matrix?\n",
+ "1. What does `export` save?\n",
+ "1. What is it called when we use a model for getting predictions, instead of training?\n",
+ "1. What are IPython widgets?\n",
+ "1. When might you want to use CPU for deployment? When might GPU be better?\n",
+ "1. What are the downsides of deploying your app to a server, instead of to a client (or edge) device such as a phone or PC?\n",
+ "1. What are 3 examples of problems that could occur when rolling out a bear warning system in practice?\n",
+ "1. What is \"out of domain data\"?\n",
+ "1. What is \"domain shift\"?\n",
+ "1. What are the 3 steps in the deployment process?\n",
+ "1. For a project you're interested in applying deep learning to, consider the thought experiment \"what would happen if it went really, really well?\"\n",
+ "1. Start a blog, and write your first blog post. For instance, write about what you think deep learning might be useful for in a domain you're interested in."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Consider how the Drivetrain approach maps to a project or problem you're interested in.\n",
+ "1. When might it be best to avoid certain types of data augmentation?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/03_ethics.ipynb b/03_ethics.ipynb
new file mode 100644
index 000000000..06e51edfb
--- /dev/null
+++ b/03_ethics.ipynb
@@ -0,0 +1,988 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_ethics]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data Ethics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Acknowledgement: Dr Rachel Thomas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This chapter was co-authored by Dr Rachel Thomas, the co-founder of fast.ai, and founding director of the Center for Applied Data Ethics at the University of San Francisco. It largely follows a subset of her syllabus for the \"Introduction to Data Ethics\" course that she developed."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introduction to data ethics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we discussed in chapters 1 and 2, sometimes, machine learning models can go wrong. They can have bugs. They can be presented with data that they haven't seen before, and behave in ways we don't expect. Or, they could work exactly as designed, but be used for something that you would much prefer they were never ever used for.\n",
+ "\n",
+ "Because deep learning is such a powerful tool and can be used for so many things, it becomes particularly important that we consider the consequences of our choices. The philosophical study of *ethics* is the study of right and wrong, including how we can define those terms, recognise right and wrong actions, and understand the connection between actions and consequences. The field of *data ethics* has been around for a long time, and there are many academics focused on this field. It is being used to help define policy in many jurisdictions; it is being used in companies big and small to consider how best to ensure good societal outcomes from product development; and it is being used by researchers who want to make sure that the work they are doing is used for good, and not for bad.\n",
+ "\n",
+ "As a deep learning practitioner, therefore, it is likely that at some point you are going to be put in a situation where you need to consider data ethics. So what is data ethics? It's a subfield of ethics, so let's start there."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: At university, philosophy of ethics was my main thing (it would have been the topic of my thesis, if I'd finished it, instead of dropping out to join the real-world). Based on the years I spent studying ethics, I can tell you this: no one really agrees on what right and wrong are, whether they exist, how to spot them, which people are good, and which bad, or pretty much anything else. So don't expect too much from the theory! We're going to focus on examples and thoughts starters here, not theory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In answering the question [What is Ethics](https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/what-is-ethics/), The Markkula Center for Applied Ethics says that *ethics* refers to:\n",
+ "\n",
+ "- Well-founded standards of right and wrong that prescribe what humans ought to do, and\n",
+ "- The study and development of one's ethical standards.\n",
+ "\n",
+ "There is no list of right answers for ethics. There is no list of dos and don'ts. Ethics is complicated, and context-dependent. It involves the perspectives of many stakeholders. Ethics is a muscle that you have to develop and practice. In this chapter, our goal is to provide some signposts to help you on that journey.\n",
+ "\n",
+ "Spotting ethical issues is best to do as part of a collaborative team. This is the only way you can really incorporate different perspectives. Different people's backgrounds will help them to see things which may not be obvious to you. Working with a team is helpful for many \"muscle building\" activities, including this one.\n",
+ "\n",
+ "This chapter is certainly not the only part of the book where we talk about data ethics, but it's good to have a place where we focus on it for a while. To get oriented, it's perhaps easiest to look at a few examples. So we picked out three that we think illustrate effectively some of the key topics."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting started with some examples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are going to start with three specific examples that illustrate three common ethical issues in tech:\n",
+ "\n",
+ "1. **Recourse processes**: Arkansas's buggy healthcare algorithms left patients stranded\n",
+ "2. **Feedback loops**: YouTube's recommendation system helped unleash a conspiracy theory boom\n",
+ "3. **Bias**: When a traditionally African-American name is searched for on Google, it displays ads for criminal background checks.\n",
+ "\n",
+ "In fact, for every concept that we introduce in this chapter, we are going to provide at least one specific example. For each one, have a think about what you could have done in this situation, and think about what kinds of obstructions there might have been to you getting that done. How would you deal with them? What would you look out for?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Bugs and recourse: Buggy algorithm used for healthcare benefits"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Verge investigated software used in over half of U.S. states to determine how much healthcare people receive, and documented their findings in an article [What Happens When an Algorithm Cuts Your Healthcare](https://www.theverge.com/2018/3/21/17144260/healthcare-medicaid-algorithm-arkansas-cerebral-palsy). After implementation of the algorithm in Arkansas, people (many with severe disabilities) drastically had their healthcare cut. For instance, Tammy Dobbs, a woman with cerebral palsy who needs an aid to help her to get out of bed, to go to the bathroom, to get food, and more, had her hours of help suddenly reduced by 20 hours a week. She couldn’t get any explanation for why her healthcare was cut. Eventually, a court case revealed that there were mistakes in the software implementation of the algorithm, negatively impacting people with diabetes or cerebral palsy. However, Dobbs and many other people reliant on these health care benefits live in fear that their benefits could again be cut suddenly and inexplicably."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Feedback loops: YouTube's recommendation system"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Feedback loops can occur when your model is controlling the next round of data you get. The data that is returned quickly becomes flawed by the software itself.\n",
+ "\n",
+ "For instance, in <> we briefly mentioned the reinforcement learning algorithm which Google introduced for YouTube's recommendation system. YouTube has 1.9bn users, who watch over 1 billion hours of YouTube videos a day. Their algorithm, which was designed to optimise watch time, is responsible for around 70% of the content that is watched. It led to out-of-control feedback loops, leading the New York Times to run the headline \"YouTube Unleashed a Conspiracy Theory Boom. Can It Be Contained?\". Ostensibly recommendation systems are predicting what content people will like, but they also have a lot of power in determining what content people even see."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Bias: Professor Lantanya Sweeney \"arrested\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dr. Latanya Sweeney is a professor at Harvard and director of their data privacy lab. In the paper [Discrimination in Online Ad Delivery](https://arxiv.org/abs/1301.6822) she describes her discovery that googling her name resulted in advertisements saying \"Latanya Sweeney arrested\" even although she is the only Latanya Sweeney and has never been arrested. However when she googled other names, such as Kirsten Lindquist, she got more neutral ads, even though Kirsten Lindquist has been arrested three times."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Screenshot](\"images/ethics/image1.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Being a computer scientist, she studied this systematically, and looked at over 2000 names. She found that this pattern held where historically black names received advertisements suggesting that the person had a criminal record. Whereas, white names had more neutral advertisements.\n",
+ "\n",
+ "This is an example of bias. It can make a big difference to people's lives — for instance, if a job applicant is googled that it may appear that they have a criminal record when they do not."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## So what?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One very natural reaction to considering these issues is: \"So what? What's that got to do with me? I'm a data scientist, not a politician. I'm not the senior executive at my company who make the decisions about what we do. I'm just trying to build the most predictive model I can.\"\n",
+ "\n",
+ "These are very reasonable questions. But we're going to try to convince you that the answer is: everybody who is training models absolutely needs to consider how their model will be used. And to consider how to best ensure that it is used as positively as possible. There are things you can do. And if you don't do these things, then things can go pretty bad.\n",
+ "\n",
+ "One particularly hideous example of what happens when technologists focus on technology at all costs is the story of IBM and Nazi Germany. A Swiss judge ruled \"It does not thus seem unreasonable to deduce that IBM's technical assistance facilitated the tasks of the Nazis in the commission of their crimes against humanity, acts also involving accountancy and classification by IBM machines and utilized in the concentration camps themselves.\"\n",
+ "\n",
+ "IBM, you see, supplied the Nazis with data tabulation products necessary to track the extermination of Jews and other groups on a massive scale. This was driven from the top of the company, with marketing to Hitler and his leadership team. Company President Thomas Watson personally approved the 1939 release of special IBM alphabetizing machines to help organize the deportation of Polish Jews. Pictured here is Adolf Hitler (far left) meeting with IBM CEO Tom Watson Sr. (2nd from left), shortly before Hitler awarded Watson a special “Service to the Reich” medal in 1937:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"A](\"images/ethics/image2.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But it also happened throughout the organization. IBM and its subsidiaries provided regular training and maintenance on-site at the concentration camps: printing off cards, configuring machines, and repairing them as they broke frequently. IBM set up categorizations on their punch card system for the way that each person was killed, which group they were assigned to, and the logistical information necessary to track them through the vast Holocaust system. IBM's code for Jews in the concentration camps was 8, where around 6,000,000 were killed. Its code for Romanis was 12 (they were labeled by the Nazis as \"asocials\", with over 300,000 killed in the *Zigeunerlager*, or “Gypsy camp”). General executions were coded as 4, death in the gas chambers as 6."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Picture](\"images/ethics/image3.jpeg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Of course, the project managers and engineers and technicians involved were just living their ordinary lives. Caring for their families, going to the church on Sunday, doing their jobs as best as they could. Following orders. The marketers were just doing what they could to meet their business development goals. Edwin Black, author of \"IBM and the Holocaust\", said: \"To the blind technocrat, the means were more important than the ends. The destruction of the Jewish people became even less important because the invigorating nature of IBM's technical achievement was only heightened by the fantastical profits to be made at a time when bread lines stretched across the world.\"\n",
+ "\n",
+ "Step back for a moment and consider: how would you feel if you discovered that you had been part of a system that ending up hurting society? Would you even know? Would you be open to finding out? How can you help make sure this doesn't happen? We have described the most extreme situation here in Nazi Germany, but there are many negative societal consequences happening due to AI and machine learning right now, some of which we'll describe in this chapter.\n",
+ "\n",
+ "It's not just a moral burden either. Sometimes, technologists very directly pay for their actions. For instance, the first person who was jailed as a result of the Volkswagen scandal, where the car company cheated on their diesel emissions tests, was not the manager that oversaw the project, or an executive at the helm of the company. It was one of the engineers, James Liang, who just did what he was told.\n",
+ "\n",
+ "On the other hand, if a project you are involved in turns out to make a huge positive impact on even one person, this is going to make you feel pretty great!\n",
+ "\n",
+ "Okay, so hopefully we have convinced you that you ought to care. Now the question is: can you actually do anything can you make an impact beyond just maximising the predictive power of your models? Consider the pipeline are steps that occurs between the development of a model or an algorithm by a researcher or practitioner, and the point at which this work is actually used to make some decision. Normally there is a very long chain from one end to the other. This is especially true if you are a researcher where you don't even know if your research will ever get used for anything. It's especially tricky if you're involved in data collection, which is even earlier in the pipeline.\n",
+ "\n",
+ "Data often ends up being used for different purposes than why it was originally collected. IBM began selling to Nazi Germany well before the Holocaust, including helping with Germany’s 1933 census conducted by Adolf Hitler, which was effective at identifying far more Jewish people than had previously been recognized in Germany. US census data was used to round up Japanese-Americans (who were US citizens) for internment during World War II. It is important to recognize how data and images collected can be weaponized later. Columbia professor [Tim Wu wrote](https://www.nytimes.com/2019/04/10/opinion/sunday/privacy-capitalism.html) that “You must assume that any personal data that Facebook or Android keeps are data that governments around the world will try to get or that thieves will try to steal.”"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Integrating machine learning with product design"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Presumably the reason you're doing this work is because you hope it will be used for something. Otherwise, you're just wasting your time. So, let's start with the assumption that your work will end up somewhere. Now, as you are collecting your data and developing your model, you are making lots of decisions. What level of aggregation will you store your data at? What loss function should you use? What validation and training sets should you use? Should you focus on simplicity of implementation, speed of inference, or accuracy of the model? How will your model handle out of domain data items? Can it be fine-tuned, or must it be retrained from scratch over time?\n",
+ "\n",
+ "These are not just algorithm questions. They are data product design questions. But the product managers, executives, judges, journalists, doctors… whoever ends up developing and using the system of which your model is a part will not be well-placed to understand the decisions that you made, let alone change them.\n",
+ "\n",
+ "For instance, two studies found that Amazon’s facial recognition software produced [inaccurate](https://www.nytimes.com/2018/07/26/technology/amazon-aclu-facial-recognition-congress.html) and [racially biased results](https://www.theverge.com/2019/1/25/18197137/amazon-rekognition-facial-recognition-bias-race-gender). Amazon claimed that the researchers should have changed the default parameters. However, it turned out that [Amazon was not instructing police departments](https://gizmodo.com/defense-of-amazons-face-recognition-tool-undermined-by-1832238149) that use its software to do this either. There was, presumably, a big distance between the researchers that developed these algorithms, and the Amazon documentation staff that wrote the guidelines provided to the police. A lack of tight integration led to serious problems for society, the police, and Amazon themselves. It turned out that their system erroneously *matched* 28 members of congress to criminal mugshots! (And these members of congress wrongly matched to criminal mugshots disproportionately included people of color.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Picture](\"images/ethics/image4.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data scientists need to be part of a cross disciplinary team. And researchers need to work closely with the kinds of people who will end up using their research. Better still is if the domain experts themselves have learnt enough to be able to train and debug some models themselves — hopefully there's a few of you reading this book right now!\n",
+ "\n",
+ "The modern workplace is a very specialised place. Everybody tends to have very well-defined jobs to perform. Especially in large companies, it can be very hard to know what all the pieces of the puzzle are. Sometimes companies even intentionally obscure the overall project goals that are being worked on, if they know that their employees are not going to like the answers. This is sometimes done by compartmentalising every piece as much as possible\n",
+ "\n",
+ "In other words, we're not saying that any of this is easy. It's hard. It's really hard. We all have to do our best. And with often seen that the people who do get involved in the higher-level context of these projects, and attempt to develop cross disciplinary capabilities and teams, become some of the most important and well rewarded parts of their organisations. It's the kind of work that tends to be highly appreciated by senior executives, even if it is considered, sometimes, rather uncomfortable by middle management."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Topics in Data Ethics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data ethics is a big field, and we can't cover everything. Instead, we're going to pick a few topics which we think are particularly relevant:\n",
+ "- need for recourse and accountability\n",
+ "- feedback loops\n",
+ "- bias\n",
+ "- disinformation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Errors and recourse"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In a complex system it is easy for no one person to feel responsible for outcomes. While this is understandable, it does not lead to good results. In the example above of the Arkansas healthcare system in which a bug led to people with cerebral palsy losing access to needed care, the creator of the algorithm blamed government officials, and government officials could blame those who implemented the software. NYU professor danah boyd described this phenomenon: \"bureaucracy has often been used to evade responsibility, and today's algorithmic systems are extending bureaucracy.\"\n",
+ "\n",
+ "An additional reason why recourse is so necessary, is because data often contains errors. Mechanisms for audits and error-correction are crucial. A database of suspected gang members maintained by California law enforcement officials was found to be full of errors, including 42 babies who had been added to the database when they were less than 1 year old (28 of whom were marked as “admitting to being gang members”). In this case, there was no process in place for correcting mistakes or removing people once they’ve been added. Another example is the US credit report system; in a large-scale study of credit reports by the FTC in 2012, it was found that 26% of consumers had at least one mistake in their files, and 5% had errors that could be devastating. Yet, the process of getting such errors corrected is incredibly slow and opaque. When public-radio reporter Bobby Allyn discovered that he was erroneously listed as having a firearms conviction, it took him \"more than a dozen phone calls, the handiwork of a county court clerk and six weeks to solve the problem. And that was only after I contacted the company’s communications department as a journalist.\" (as covered in the article [How the careless errors of credit reporting agencies are ruining people’s lives](https://www.washingtonpost.com/posteverything/wp/2016/09/08/how-the-careless-errors-of-credit-reporting-agencies-are-ruining-peoples-lives/))\n",
+ "\n",
+ "As machine learning practitioners, we do not always think of it as our responsibility to understand how our algorithms and up being implemented in practice. But we need to."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Feedback loops"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The New York Times published another article on YouTube's recommendation system, titled [On YouTube’s Digital Playground, an Open Gate for Pedophiles](https://www.nytimes.com/2019/06/03/world/americas/youtube-pedophiles.html). The article started with this chilling story:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> : Christiane C. didn’t think anything of it when her 10-year-old daughter and a friend uploaded a video of themselves playing in a backyard pool… A few days later… the video had thousands of views. Before long, it had ticked up to 400,000... “I saw the video again and I got scared by the number of views,” Christiane said. She had reason to be. YouTube’s automated recommendation system… had begun showing the video to users who watched other videos of prepubescent, partially clothed children, a team of researchers has found.\n",
+ "\n",
+ "> : On its own, each video might be perfectly innocent, a home movie, say, made by a child. Any revealing frames are fleeting and appear accidental. But, grouped together, their shared features become unmistakable."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "YouTube's recommendation algorithm had begun curating playlists for pedophiles, picking out innocent home videos that happened to contain prepubescent, partially clothed children. \n",
+ "\n",
+ "No one at Google planned to create a system that turned family videos into porn for pedophiles. So what happened?\n",
+ "\n",
+ "Part of the problem here is the centrality of metrics in driving a financially important system. When an algorithm has a metric to optimise, as you have seen, it will do everything it can to optimise that number. This tends to lead to all kinds of edge cases, and humans interacting with a system will search for, find, and exploit these edge cases and feedback loops for their advantage.\n",
+ "\n",
+ "There are signs that this is exactly what has happened with YouTube's recommendation system. The Guardian ran an article [How an ex-YouTube insider investigated its secret algorithm](https://www.theguardian.com/technology/2018/feb/02/youtube-algorithm-election-clinton-trump-guillaume-chaslot) about Guillaume Chaslot, an ex-YouTube engineer who created AlgoTransparency, which tracks these issues. Chaslot published this chart, following the release of Robert Mueller's \"Report on the Investigation Into Russian Interference in the 2016 Presidential Election\":"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Coverage](\"images/ethics/image18.jpeg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Russia Today's coverage of the Mueller report was an extreme outlier in how many channels were recommending it. This suggests the possibility that Russia Today, a state-owned Russia media outlet, has been successful in gaming YouTube's recommendation algorithm. The lack of transparency of systems like this make it hard to uncover the kinds of problems that we're discussing.\n",
+ "\n",
+ "Another example of a feedbakc loop is a predictive policing algorithm that predicts more crime in certain neighborhoods, causing more police officers to be sent to those neighborhoods, which can result in more crime being recorded in those neighborhoods, and so on. University of Utah computer science processor Suresh Venkatasubramanian says about this: \"Predictive policing is aptly named: it is predicting future policing, not future crime.”\n",
+ "\n",
+ "There are positive examples of people and organizations attempting to combat these problems. Evan Estola, lead machine learning engineer at Meetup, [discussed the example](https://www.youtube.com/watch?v=MqoRzNhrTnQ) of men expressing more interest than women in tech meetups. Meetup’s algorithm could recommend fewer tech meetups to women, and as a result, fewer women would find out about and attend tech meetups, which could cause the algorithm to suggest even fewer tech meetups to women, and so on in a self-reinforcing feedback loop. Evan and his team made the ethical decision for their recommendation algorithm to not create such a feedback loop, but explicitly not using gender for that part of their model. It is encouraging to see a company not just unthinkingly optimize a metric, but to consider their impact. \"You need to decide which feature not to use in your algorithm… the most optimal algorithm is perhaps not the best one to launch into production\", he said.\n",
+ "\n",
+ "While Meetup chose to avoid such an outcome, Facebook provides an example of allowing a runaway feedback loop to run wild. Facebook radicalizes users interested in one conspiracy theory by introducing them to more. As [Renee DiResta, a researcher on proliferation of disinformation, writes](https://www.fastcompany.com/3059742/social-network-algorithms-are-distorting-reality-by-boosting-conspiracy-theories):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> : \"once people join a single conspiracy-minded \\[Facebook\\] group, they are algorithmically routed to a plethora of others. Join an anti-vaccine group, and your suggestions will include anti-GMO, chemtrail watch, flat Earther (yes, really), and ‘curing cancer naturally’ groups. Rather than pulling a user out of the rabbit hole, the recommendation engine pushes them further in.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Discussions of bias online tend to get pretty confusing pretty fast. The word bias mean so many different things. Statisticians often think that when data ethicists are talking about bias that they're talking about the statistical definition of the term bias. But they're not. And they're certainly not talking about the bias is that appear in the weights and bias is which are the parameters of your model!\n",
+ "\n",
+ "What they're talking about is the social science concept of bias. In [A Framework for Understanding Unintended Consequences of Machine Learning](https://arxiv.org/abs/1901.10002) MIT's Suresh and Guttag describe six types of bias in machine learning, summarized in this figure from their paper:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"A](\"images/ethics/image5.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll discuss four of these types of bias here (see the paper for details on the others)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Historical bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Historical bias* comes from the fact that people are biased, processes are biased, and society is biased. Suresh and Guttag say: \"Historical bias is a fundamental, structural issue with the first step of the data generation process and can exist even given perfect sampling and feature selection\".\n",
+ "\n",
+ "For instance, here's a few examples of historical *race bias* in the US, from the NY Times article [Racial Bias, Even When We Have Good Intentions](https://www.nytimes.com/2015/01/04/upshot/the-measuring-sticks-of-racial-bias-.html), by the University of Chicago's Sendhil Mullainathan:\n",
+ "\n",
+ " - When doctors were shown identical files, they were much less likely to recommend cardiac catheterization (a helpful procedure) to Black patients\n",
+ " - When bargaining for a used car, Black people were offered initial prices $700 higher and received far smaller concessions\n",
+ " - Responding to apartment-rental ads on Craigslist with a Black name elicited fewer responses than with a white name\n",
+ " - An all-white jury was 16 points more likely to convict a Black defendant than a white one, but when a jury had 1 Black member, it convicted both at same rate.\n",
+ "\n",
+ "The COMPAS algorithm, widely used for sentencing and bail decisions in the US, is an example of an important algorithm which, when tested by ProPublica, showed clear racial bias in practice:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Table](\"images/ethics/image6.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Any dataset involving humans can have this kind of bias, such as medical data, sales data housing data, political data, and so on. Because underlying bias is so pervasive, bias in datasets is very pervasive. Racial bias even turns up in computer vision, as shown in this example of auto-categorized photos shared on Twitter by a Google Photos user:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Screenshot](\"images/ethics/image7.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Yes, that is showing what you think it is: Google Photos classified a Black user's photo with their friend as \"gorrilas\"! This algorithmic mis-step got a lot of attention in the media. “We’re appalled and genuinely sorry that this happened,” a company spokeswoman said. “There is still clearly a lot of work to do with automatic image labeling, and we’re looking at how we can prevent these types of mistakes from happening in the future.”\n",
+ "\n",
+ "Unfortunately, fixing problems in machine learning systems when the input data has problems is hard. Google's first attempt didn't inspire confidence, as covered by The Guardian:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Pictures](\"images/ethics/image8.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These kinds of problem are certainly not limited to just Google. MIT researchers studied the most popular online computer vision APIs to see how accurate they were. But they didn't just calculate a single accuracy number—instead, they looked at the accuracy across four different groups:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Table](\"images/ethics/image9.jpeg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "IBM's system, for instance, had a 34.7% error rate for darker females, vs 0.3% for lighter males—over 100 times more errors! Some people incorrectly reacted to these experiments by claiming that the difference was simply because darker skin is harder for computers to recognise. However, what actually happened, is after the negative publicity that this result created, all of the companies in question dramatically improved their models for darker skin, such that one year later they were nearly as good as for lighter skin. So what this actually showed is that the developers failed to utilise datasets containing enough darker faces, or test their product with darker faces.\n",
+ "\n",
+ "One of the MIT researchers, Joy Buolamwini, warned, \"We have entered the age of automation overconfident yet underprepared. If we fail to make ethical and inclusive artificial intelligence, we risk losing gains made in civil rights and gender equity under the guise of machine neutrality\".\n",
+ "\n",
+ "Part of the issue appears to be a systematic imbalance in the make up of popular datasets used for training models. The abstract to the paper [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://arxiv.org/abs/1711.08536) states, \"We analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit an observable amerocentric and eurocentric representation bias. Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales\". Here is one of the charts from the paper, showing the geographic make up of what was, at the time (and still, as this book is being written), the two most important image datasets for training models:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Grpahs](\"images/ethics/image10.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The vast majority of the images are from the United States and other Western countries, leading to models trained on ImageNet performing worse on scenes from other countries and cultures. For instance, [research](https://arxiv.org/pdf/1906.02659.pdf) found that such models are worse at identifying household items (such as soap, spices, sofas, or beds) from lower-income countries. Below is an image from the paper, [Does Object Recognition Work for Everyone?](https://arxiv.org/pdf/1906.02659.pdf)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Figure](\"images/ethics/image17.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we will discuss shortly, in addition, the vast majority of AI researchers and developers are young white men. Most projects that we have seen do most user testing using friends and families of the immediate product development group. Given this, the kinds of problems we saw above should not be surprising.\n",
+ "\n",
+ "Similar historical bias is found in the texts used as data for natural language processing models. This crops up in downstream machine learning tasks in many ways. For instance, until last year Google Translate showed systematic bias in how it translated the Turkish gender-neutral pronoun \"bir\" into English. For instance, when applied to jobs which are often associated with males, it used \"he\", and when applied to jobs which are often associated with females, it used \"she\":"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"images/ethics/image11.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also see this kind of bias in online advertisements. For instance, a study in 2019 found that even when the person placing the ad does not intentionally discriminate, Facebook will show the ad to very different audiences used on race and gender. Housing ads with the same text, but changing the picture, between a white family and a black family, were shown to racially different audiences."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Measurement bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the paper [Does Machine Learning Automate Moral Hazard and Error](https://scholar.harvard.edu/files/sendhil/files/aer.p20171084.pdf) in *American Economic Review*, the authors look at a model that tries to answer the question: using historical EHR data, what factors are most predictive of stroke? This are the top predictors from the model:\n",
+ "\n",
+ " - Prior Stroke\n",
+ " - Cardiovascular disease\n",
+ " - Accidental injury\n",
+ " - Benign breast lump\n",
+ " - Colonoscopy\n",
+ " - Sinusitis\n",
+ "\n",
+ "However, only the top two have anything to do with a stroke! Based on what we've studied so far, you can probably guess why. We haven’t really measured *stroke*, which occurs when a region of the brain is denied oxygen due to an interruption in the blood supply. What we’ve measured is who: had symptoms, went to a doctor, got the appropriate tests, AND received a diagnosis of stroke. Actually having a stroke is not the only thing correlated with this complete list — it's also correlated with being the kind of person who actually goes to the doctor (which is influenced by who has access to healthcare, can afford their co-pay, doesn't experience racial or gender-based medical discrimination, and more)! If you are likely to go to the doctor for an *accidental injury*, then you are likely to also go the doctor when you are having a stroke.\n",
+ "\n",
+ "This is an example of *measurement bias*. It occurs when our models make mistakes because we are measuring the wrong thing, or measuring it in the wrong way, or incorporating that measurement into our model inappropriately."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Aggregation Bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Aggregation bias* occurs when models do not aggregate data in a way that incorporates all of the appropriate factors, or when a model does not include the necessary interaction terms, nonlinearities, or so forth. This can particularly occur in medical settings. For instance, the way diabetes is treated is often based on simple univariate statistics and studies involving small groups of heterogeneous people. Analysis of results is often done in a way that does not take account of different ethnicities or genders. However it turns out that diabetes patients have [different complications across ethnicities](https://www.ncbi.nlm.nih.gov/pubmed/24037313), and HbA1c levels (widely used to diagnose and monitor diabetes) [differ in complex ways across ethnicities and genders](https://www.ncbi.nlm.nih.gov/pubmed/22238408). This can result in people being misdiagnosed or incorrectly treated because medical decisions are based on a model which does not include these important variables and interactions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Representation Bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The abstract of the paper [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://arxiv.org/abs/1901.09451) notes that there is gender imbalance in occupations (e.g. females are more likely to be nurses, and males are more likely to be pastors), and says that: \"differences in true positive rates between genders are correlated with existing gender imbalances in occupations, which may compound these imbalances\".\n",
+ "\n",
+ "What this is saying is that the researchers noticed that models predicting occupation did not only reflect the actual gender imbalance in the underlying population, but actually amplified it! This is quite common, particularly for simple models. When there is some clear, easy to see underlying relationship, a simple model will often simply assume that that relationship holds all the time. As the show with the paper, for occupations which had a higher percentage of females, the model tended to overestimate the prevalence of that occupation:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Graph](\"images/ethics/image12.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For example, in the training dataset, 14.6% of surgeons were women, yet in the model predictions, only 11.6% of the true positives were women."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Addressing different types of bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Different types of bias require different approaches for mitigation. While gathering a more diverse dataset can address representation bias, this would not help with historical bias or measurement bias. All datasets contain bias. There is no such thing as a completely de-biased dataset. Many researchers in the field have been converging on a set of proposals towards better documenting the decisions, context, and specifics about how and why a particular dataset was created, what scenarios it is appropriate to use in, and what the limitations are. This way, those using the dataset will not be caught off-guard by its biases and limitations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Humans are biased, so does algorithmic bias matter?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We often hear this question — \"humans are biased, so does algorithmic bias even matter?\" This comes up so often, there must be some reasoning that makes sense to the people that ask it, but it doesn't seem very logically sound to us! Independently of whether this is logically sound, it's important to realise that algorithms and people are different. Machine learning, particularly so. Consider these points about machine learning algorithms:\n",
+ "\n",
+ " - *Machine learning can create feedback loops*: small amounts of bias can very rapidly, exponentially increase due to feedback loops\n",
+ " - *Machine learning can amplify bias*: human bias can lead to larger amounts of machine learning bias\n",
+ " - *Algorithms & humans are used differently*: human decision makers and algorithmic decision makers are not used in a plug-and-play interchangeable way in practice. For instance, algorithmic decisions are more likely to be implemented at scale and without a process for recourse. Furthermore, people are more likely to mistakenly believe that the result of an algorithm is objective and error-free.\n",
+ " - *Technology is power*. And with that comes responsibility.\n",
+ "\n",
+ "As the Arkansas healthcare example showed, machine learning is often implemented in practice not because it leads to better outcomes, but because it is cheaper and more efficient. Cathy O'Neill, in her book *Weapons of Math Destruction*, described the pattern of how the privileged are processed by people, the poor are processed by algorithms. This is just one of a number of ways that algorithms are used differently than human decision makers. Others include:\n",
+ "\n",
+ " - People are more likely to assume algorithms are objective or error-free (even if they’re given the option of a human override)\n",
+ " - Algorithms are more likely to be implemented with no appeals process in place\n",
+ " - Algorithms are often used at scale\n",
+ " - Algorithmic systems are cheap."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data contains errors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Because data is likely to contain errors, mechanisms for audits and error-correction are important. A database of suspected gang members maintained by California law enforcement officials was found to be full of errors, including 42 babies who had been added to the database when they were less than 1 year old (28 of whom were marked as *admitting to being gang members*). In this case, there was no process in place for correcting mistakes or removing people once they’ve been added. Another example is the US credit report system; in a large-scale study of credit reports by the FTC in 2012, it was found that 26% of consumers had at least one mistake in their files, and 5% had errors that could be devastating. Yet, the process of getting such errors corrected is incredibly slow and opaque. When public-radio reporter Bobby Allyn discovered that he was erroneously listed as having a firearms conviction, it took him \"more than a dozen phone calls, the handiwork of a county court clerk and six weeks to solve the problem. And that was only after I contacted the company’s communications department as a journalist.\" (as covered in the article [How the careless errors of credit reporting agencies are ruining people’s lives](https://www.washingtonpost.com/posteverything/wp/2016/09/08/how-the-careless-errors-of-credit-reporting-agencies-are-ruining-peoples-lives/))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Disinformation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Disinformation* has a history stretching back hundreds or even thousands of years. It is not necessarily about getting someone to believe something false, but rather, often to sow disharmony and uncertainty, and to get people to give up on seeking the truth. Receiving conflicting accounts can lead people to assume that they can never know what to trust.\n",
+ "\n",
+ "Some people think disinformation is primarily about false information or *fake news*, but in reality, disinformation can often contain seeds of truth, or involve half-truths taken out of context. Ladislav Bittman, who was an intelligence officer in the USSR who later defected to the United States and wrote some books in the 1970s and 1980s on the role of disinformation in Soviet propaganda operations. He said, \"Most campaigns are a carefully designed mixture of facts, half-truths, exaggerations, & deliberate lies.\"\n",
+ "\n",
+ "In the United States this has hit close to home in recent years, with the FBI detailing a massive disinformation campaign linked to Russia in the 2016 US election. Understanding the disinformation that was used in this campaign is very educational. For instance, the FBI found that the Russian disinformation campaign often organized two separate fake *grass roots* protests, one for each side of an issue, and got them to protest at the same time! The Houston Chronicle reported on one of these odd events:\n",
+ "\n",
+ "> : A group that called itself the \"Heart of Texas\" had organized it on social media — a protest, they said, against the \"Islamization\" of Texas. On one side of Travis Street, I found about 10 protesters. On the other side, I found around 50 counterprotesters. But I couldn't find the rally organizers. No \"Heart of Texas.\" I thought that was odd, and mentioned it in the article: What kind of group is a no-show at its own event? Now I know why. Apparently, the rally's organizers were in Saint Petersburg, Russia, at the time. \"Heart of Texas\" is one of the internet troll groups cited in Special Prosecutor Robert Mueller's recent indictment of Russians attempting to tamper with the U.S. presidential election."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Screenshot](\"images/ethics/image13.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Disinformation often involves coordinated campaigns of inauthentic behavior. For instance, fraudulent accounts may try to make it seem like many people hold a particular viewpoint. While most of us like to think of ourselves as independent-minded, in reality we evolved to be influenced by others in our in-group, and in opposition to those in our out-group. Online discussions can influence our viewpoints, or alter the range of what we consider acceptable viewpoints. Humans are social animals, and as social animals we are extremely influenced by the people around us. Increasingly, radicalisation occurs in online environments. So influence is coming from people in the virtual space of online forums and social networks.\n",
+ "\n",
+ "Disinformation through auto-generated text is a particularly significant issue, due to the greatly increased capability provided by deep learning. We discuss this issue in depth when we learn to create language models, in <>.\n",
+ "\n",
+ "One proposed approach is to develop some form of digital signature, implement it in a seamless way, and to create norms that we should only trust content which has been verified. Head of the Allen Institute on AI, Oren Etzioni, wrote such a proposal in an article titled [How Will We Prevent AI-Based Forgery?](https://hbr.org/2019/03/how-will-we-prevent-ai-based-forgery), \"AI is poised to make high-fidelity forgery inexpensive and automated, leading to potentially disastrous consequences for democracy, security, and society. The specter of AI forgery means that we need to act to make digital signatures de rigueur as a means of authentication of digital content.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What to do"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Mistakes happen. Finding out about them, and dealing with them, needs to be part of the design of any system that includes machine learning (and many other systems too). The issues raised within data ethics are often complex and interdisciplinary, but it is crucial that we work to address them.\n",
+ "\n",
+ "So what can we do? This is a big topic, but a few steps towards addressing ethical issues are:\n",
+ "\n",
+ "- analyze a project you are working on\n",
+ "- implement processes at your company to find and address ethical risks\n",
+ "- support good policy\n",
+ "- increase diversity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Analyze a project you are working on"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's easy to miss important issues when considered ethical implications of your work. One thing that helps enormously is simply asking the right questions. Rachel Thomas recommends considering the following questions throughout the development of a data project:\n",
+ "\n",
+ " - Should we even be doing this?\n",
+ " - What bias is in the data?\n",
+ " - Can the code and data be audited?\n",
+ " - What are error rates for different sub-groups?\n",
+ " - What is the accuracy of a simple rule-based alternative?\n",
+ " - What processes are in place to handle appeals or mistakes?\n",
+ " - How diverse is the team that built it?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Processes to implement"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Markkula Center has released [An Ethical Toolkit for Engineering/Design Practice](https://www.scu.edu/ethics-in-technology-practice/ethical-toolkit/), which includes some concrete practices to implement at your company, including regularly scheduled ethical risk sweeps to proactively search for ethical risks (in a manner similar to cybersecurity penetration testing), expanding the ethical circle to include the perspectives of a variety of stakeholders, and considering the terrible people (how could bad actors abuse, steal, misinterpret, hack, destroy, or weaponize what you are building?). \n",
+ "\n",
+ "Even if you don't have a diverse team, you can still try to pro-actively include the perspectives of a wider group, considering questions such as these (provided by the Markkula Center):\n",
+ "\n",
+ " - Whose interests, desires, skills, experiences and values have we simply assumed, rather than actually consulted?\n",
+ " - Who are all the stakeholders who will be directly affected by our product? How have their interests been protected? How do we know what their interests really are—have we asked?\n",
+ " - Who/which groups and individuals will be indirectly affected in significant ways?\n",
+ " - Who might use this product that we didn’t expect to use it, or for purposes we didn’t initially intend?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Ethical Lenses"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another useful resource from the Markkula Center is [Conceptual Frameworks in Technology and Engineering Practice](https://www.scu.edu/ethics-in-technology-practice/conceptual-frameworks/). This considers how different foundational ethical lenses can help identify concrete issues, and lays out the following approaches and key questions:\n",
+ "\n",
+ " - The Rights Approach: Which option best respects the rights of all who have a stake?\n",
+ " - The Justice Approach: Which option treats people equally or proportionately?\n",
+ " - The Utilitarian Approach: Which option will produce the most good and do the least harm?\n",
+ " - The Common Good Approach: Which option best serves the community as a whole, not just some members?\n",
+ " - The Virtue Approach: Which option leads me to act as the sort of person I want to be?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Markkula's recommendations include a deeper dive into each of these perspectives, including looking at a project based on a focus on its *consequences*:\n",
+ "\n",
+ " - Who will be directly affected by this project? Who will be indirectly affected?\n",
+ " - Will the effects in aggregate likely create more good than harm, and what types of good and harm?\n",
+ " - Are we thinking about all relevant types of harm/benefit (psychological, political, environmental, moral, cognitive, emotional, institutional, cultural)?\n",
+ " - How might future generations be affected by this project?\n",
+ " - Do the risks of harm from this project fall disproportionately on the least powerful in society? Will the benefits go disproportionately the well-off?\n",
+ " - Have we adequately considered ‘dual-use?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The alternative lens to this is the *deontological* perspective, which focuses on basic *right* and *wrong*:\n",
+ "\n",
+ " - What rights of others & duties to others must we respect?\n",
+ " - How might the dignity & autonomy of each stakeholder be impacted by this project?\n",
+ " - What considerations of trust & of justice are relevant to this design/project?\n",
+ " - Does this project involve any conflicting moral duties to others, or conflicting stakeholder rights? How can we prioritize these?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Fairness, accountability, and transparency"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The professional society for computer scientists, the ACM, runs a conference on data ethics called the \"Conference on Fairness, Accountability, and Transparency\". \"Fairness, Accountability, and Transparency\" sometimes goes under the acronym *FAT*, although nowadays it's changing to *FAccT*. Microsoft has a group focused on \"Fairness, Accountability, Transparency, and Ethics\" (FATE). The various versions of this lens have resulted in the acronym \"FAT*\" seeing wide usage. In this section, we'll use \"FAccT\" to refer to the concepts of *Fairness, Accountability, and Transparency*.\n",
+ "\n",
+ "FAccT is another lens that you may find useful in considering ethical issues. One useful resource for this is the free online book [Fairness and machine learning; Limitations and Opportunities](https://fairmlbook.org/), which \"gives a perspective on machine learning that treats fairness as a central concern rather than an afterthought.\" It also warns, however, that it \"is intentionally narrow in scope... A narrow framing of machine learning ethics might be tempting to technologists and businesses as a way to focus on technical interventions while sidestepping deeper questions about power and accountability. We caution against this temptation.\" Rather than provide an overview of the FAccT approach to ethics (which is better done in books such as the one linked above), our focus here will be on the limitations of this kind of narrow framing.\n",
+ "\n",
+ "One great way to consider whether an ethical lens is complete, is to try to come up with an example where the lens and our own ethical intuitions give diverging results. Os Keyes explored this in a graphic way in their paper [A Mulching Proposal\n",
+ "Analysing and Improving an Algorithmic System for Turning the Elderly into High-Nutrient Slurry](https://arxiv.org/abs/1908.06166). The paper's abstract says:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> : The ethical implications of algorithmic systems have been much discussed in both HCI and the broader community of those interested in technology design, development and policy. In this paper, we explore the application of one prominent ethical framework - Fairness, Accountability, and Transparency - to a proposed algorithm that resolves various societal issues around food security and population ageing. Using various standardised forms of algorithmic audit and evaluation, we drastically increase the algorithm's adherence to the FAT framework, resulting in a more ethical and beneficent system. We discuss how this might serve as a guide to other researchers or practitioners looking to ensure better ethical outcomes from algorithmic systems in their line of work."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this paper, the rather contraversial proposal (\"Turning the Elderly into High-Nutrient Slurry\") and the results (\"drastically increase the algorithm's adherence to the FAT framework, resulting in a more ethical and beneficent system\") are at odds... to say the least!\n",
+ "\n",
+ "In philosophy, and especially philosophy of ethics, this is one of the most effective tools: first, come up with a process, definition, set of questions, etc, which is designed to resolve some problem. Then try to come up with an example where that apparent solution results in a proposal that no-one would consider acceptable. This can then lead to a further refinement of the solution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Role of Policy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The ethical issues that arise in the use of automated decision systems, such as machine learning, can be complex and far-reaching. To better address them, we will need thoughtful policy, in addition to the ethical efforts of those in industry. Neither is sufficient on its own.\n",
+ "\n",
+ "Policy is the appropriate tool for addressing:\n",
+ "- Negative externalities\n",
+ "- Misaligned economic incentives\n",
+ "- “Race to the bottom” situations\n",
+ "- Enforcing accountability.\n",
+ "\n",
+ "Ethical behavior in industry is necessary as well, since:\n",
+ "- Law will not always keep up\n",
+ "- Edge cases will arise in which pracitioners must use their best judgement."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The power of diversity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Currently, less than 12% of AI researchers are women, according to a study from element AI. The statistics are similarly dire when it comes to race and age. When everybody on a team has similar backgrounds, there are likely to have similar blindspots around ethical risks. The Harvard Business Review (HBR) has published a number of studies showing many benefits of diverse teams, including:\n",
+ "\n",
+ "- [How Diversity Can Drive Innovation](https://hbr.org/2013/12/how-diversity-can-drive-innovation)\n",
+ "- [Teams Solve Problems Faster When They’re More Cognitively Diverse](https://hbr.org/2017/03/teams-solve-problems-faster-when-theyre-more-cognitively-diverse)\n",
+ "- [Why Diverse Teams Are Smarter](https://hbr.org/2016/11/why-diverse-teams-are-smarter), and\n",
+ "- [What Makes a Team Smarter? More Women](https://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women).\n",
+ "\n",
+ "Diversity can lead to problems being identified earlier, and a wider range of solutions being considered. For instance, Tracy Chou was an early engineer at Quora. She [wrote of her experiences](https://qz.com/1016900/tracy-chou-leading-silicon-valley-engineer-explains-why-every-tech-worker-needs-a-humanities-education/), describing how she advocated internally for adding a feature that would allow trolls and other bad actors to be blocked. Chou recounts, “I was eager to work on the feature because I personally felt antagonized and abused on the site (gender isn’t an unlikely reason as to why)... But if I hadn’t had that personal perspective, it’s possible that the Quora team wouldn’t have prioritized building a block button so early in its existence.” Harassment often drives people from marginalised groups off online platforms, so this functionality has been important for maintaining the health of Quora's community.\n",
+ "\n",
+ "A crucial aspect to understand is that women leave the tech industry at over twice the rate that men do, according to the Harvard business review (41% of women working in tech leave, compared to 17% of men). An analysis of over 200 books, white papers, and articles found that the reason they leave is that “they’re treated unfairly; underpaid, less likely to be fast-tracked than their male colleagues, and unable to advance.” \n",
+ "\n",
+ "Studies have confirmed a number of the factors that make it harder for women to advance in the workplace. Women receive more vague feedback and personality criticism in performance evaluations, whereas men receive actionable advice tied to business outcomes (which is more useful). Women frequently experience being excluded from more creative and innovative roles, and not receiving high visibility “stretch” assignments that are helpful in getting promoted. One study found that men’s voices are perceived as more persuasive, fact-based, and logical than women’s voices, even when reading identical scripts.\n",
+ "\n",
+ "Receiving mentorship has been statistically shown to help men advance, but not women. The reason behind this is that when women receive mentorship, it’s advice on how they should change and gain more self-knowledge. When men receive mentorship, it’s public endorsement of their authority. Guess which is more useful in getting promoted?\n",
+ "\n",
+ "As long as qualified women keep dropping out of tech, teaching more girls to code will not solve the diversity issues plaguing the field. Diversity initiatives often end up focusing primarily on white women, even although women of colour face many additional barriers. In interviews with 60 women of color who work in STEM research, 100% had experienced discrimination."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The hiring process is particularly broken in tech. One study indicative of the disfunction comes from Triplebyte, a company that helps place software engineers in companies. They conduct a standardised technical interview as part of this process. They have a fascinating dataset: the results of how over 300 engineers did on their exam, and then the results of how those engineers did during the interview process for a variety of companies. The number one finding from [Triplebyte’s research](https://triplebyte.com/blog/who-y-combinator-companies-want) is that “the types of programmers that each company looks for often have little to do with what the company needs or does. Rather, they reflect company culture and the backgrounds of the founders.”\n",
+ "\n",
+ "This is a challenge for those trying to break into the world of deep learning, since most companies' deep learning groups today were founded by academics. These groups tend to look for people \"like them\"--that is, people that can solve complex math problems and understand dense jargon. They don't always know how to spot people who are actually good at solving real problems using deep learning.\n",
+ "\n",
+ "This leaves a big opportunity for companies that are ready to look beyond status and pedigree, and focus on results!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Conclusion"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Coming from a background of working with binary logic, the lack of clear answers in ethics can be frustrating at first. Yet, the implications of how our work impacts the work, including unintended consequences and weaponization by bad actors, are some of the most important questions we can (and should!) consider. Even though there aren't any easy answers, there are definite pitfalls to avoid and practices to move towards more ethical behavior.\n",
+ "\n",
+ "One of our reviewers for this book, Fred Monroe, used to work in hedge fund trading. He told us, after reading this chapter, that many of the issues discussed here (distribution of data being dramatically different than what was trained on, impact of model and feedback loops once deployed and at scale, and so forth) were also key issues for building profitable trading models. The kinds of things you need to do to consider societal consequences are going to have a lot of overlap with things you need to do to consider organizational, market, and customer consequences too--so thinking carefully about ethics can also help you think carefully about how to make your data product successful more generally!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Does ethics provide a list of \"right answers\"?\n",
+ "1. How can working with people of different backgrounds help when considering ethical questions?\n",
+ "1. What was the role of IBM in Nazi Germany? Why did the company participate as they did? Why did the workers participate?\n",
+ "1. What was the role of the first person jailed in the VW diesel scandal?\n",
+ "1. What was the problem with a database of suspected gang members maintained by California law enforcement officials?\n",
+ "1. Why did YouTube's recommendation algorithm recommend videos of partially clothed children to pedophiles, even although no employee at Google programmed this feature?\n",
+ "1. What are the problems with the centrality of metrics?\n",
+ "1. Why did Meetup.com not include gender in their recommendation system for tech meetups?\n",
+ "1. What are the six types of bias in machine learning, according to Suresh and Guttag?\n",
+ "1. Give two examples of historical race bias in the US\n",
+ "1. Where are most images in Imagenet from?\n",
+ "1. In the paper \"Does Machine Learning Automate Moral Hazard and Error\" why is sinusitis found to be predictive of a stroke?\n",
+ "1. What is representation bias?\n",
+ "1. How are machines and people different, in terms of their use for making decisions?\n",
+ "1. Is disinformation the same as \"fake news\"?\n",
+ "1. Why is disinformation through auto-generated text a particularly significant issue?\n",
+ "1. What are the five ethical lenses described by the Markkula Center?\n",
+ "1. Where is policy an appropriate tool for addressing data ethics issues?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Read the article \"What Happens When an Algorithm Cuts Your Healthcare\". How could problems like this be avoided in the future?\n",
+ "1. Research to find out more about YouTube's recommendation system and its societal impacts. Do you think recommendation systems must always have feedback loops with negative results? What approaches could Google take? What about the government?\n",
+ "1. Read the paper \"Discrimination in Online Ad Delivery\". Do you think Google should be considered responsible for what happened to Dr Sweeney? What would be an appropriate response?\n",
+ "1. How can a cross-disciplinary team help avoid negative consequences?\n",
+ "1. Read the paper \"Does Machine Learning Automate Moral Hazard and Error\" in American Economic Review. What actions do you think should be taken to deal with the issues identified in this paper?\n",
+ "1. Read the article \"How Will We Prevent AI-Based Forgery?\" Do you think Etzioni's proposed approach could work? Why?\n",
+ "1. Complete the section \"Analyze a project you are working on\" in this chapter.\n",
+ "1. Consider whether your team could be more diverse. If so, what approaches might help?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Section 1: that's a wrap!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Congratulations! You've made it to the end of the first section of the book. In this section we've tried to show you what deep learning can do, and how you can use it to create real applications and products. At this point, you will get a lot more out of the book if you spend some time trying out what you've learnt. Perhaps you have already been doing this as you go along — in which case, great! But if not, that's no problem either… Now is a great time to start experimenting yourself.\n",
+ "\n",
+ "If you haven't been to the book website yet, head over there now. Remember, you can find it here: [book.fast.ai](https;//book.fast.ai). It's really important that you have got yourself set up to run the notebooks. Becoming an effective deep learning practitioner is all about practice. So you need to be training models. So please go get the notebooks running now if you haven't already! And also have a look on the website for any important updates or notices; deep learning changes fast, and we can't change the words that in this book, so the website is where you need to look to ensure you have the most up-to-date information.\n",
+ "\n",
+ "Make sure that you have completed the following steps:\n",
+ "\n",
+ "- Connected to one of the GPU Jupyter servers recommended on the book website\n",
+ "- Run the first notebook yourself\n",
+ "- Uploaded an image that you find in the first notebook; then try a few different images of different kinds to see what happens\n",
+ "- Run the second notebook, collecting your own dataset based on image search queries that you come up with\n",
+ "- Thought about how you can use deep learning to help you with your own projects, including what kinds of data you could use, what kinds of problems may come up, and how you might be able to mitigate these issues in practice.\n",
+ "\n",
+ "In the next section of the book we will learn about how and why deep learning works, instead of just seeing how we can use it in practice. Understanding the how and why is important for both practitioners and researchers, because in this fairly new field nearly every project requires some level of customisation and debugging. The better you understand the foundations of deep learning, the better your models will be. These foundations are less important for executives, product managers, and so forth (although still useful, so feel free to keep reading!), but they are critical for anybody who is actually training and deploying models themselves."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/04_mnist_basics.ipynb b/04_mnist_basics.ipynb
new file mode 100644
index 000000000..108acc1d2
--- /dev/null
+++ b/04_mnist_basics.ipynb
@@ -0,0 +1,4881 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from fastai2.vision.all import *\n",
+ "from utils import *\n",
+ "\n",
+ "matplotlib.rc('image', cmap='Greys')"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_mnist_basics]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Under the hood: training a digit classifier"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pixels: the foundations of computer vision"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we’ve seen what it looks like to actually train a variety of models, let’s now dig under the hood and see exactly what is going on. We’ll start with computer vision, and will use that to introduce many of the key concepts of deep learning. In future chapters we’ll do deep dives into other applications as well, and we’ll see how to use these insights to both improve our model’s accuracy, speed up its training, and turn it into a real working web application.\n",
+ "\n",
+ "In order to understand what happens in a computer vision model, we first have to understand how computers handle images. We'll use one of the most famous datasets in computer vision, [MNIST](https://en.wikipedia.org/wiki/MNIST_database), for our experiments. MNIST contains hand-written digits, collected by the National Institute of Standards and Technology, and collated into a machine learning dataset by Yann Lecun and his colleagues. Lecun used MNIST in 1998 to demonstrate [Lenet 5](http://yann.lecun.com/exdb/lenet/), the first computer system to demonstrate practically useful recognition of hand-written digit sequences. This was one of the most important breakthroughs in the history of AI."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sidebar: Tenacity and deep learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The story of deep learning is one of tenacity and grit from a handful of dedicated researchers. After early hopes (and hype!) neural networks went out of favor in the 1990's and 2000's, and just a handful of researchers kept trying to make them work well. Three of them, Yann Lecun, Geoff Hinton, and Yoshua Bengio were awarded the highest honor in computer science, the Turing Award (generally considered the \"Nobel Prize of computer science\") after triumphing despite the deep skepticism and disinterest of the wider machine learning and statistics community.\n",
+ "\n",
+ "![\"Picture](\"images/turing_300.jpg\")
\n",
+ "\n",
+ "Geoff Hinton has told of how even academic papers showing dramatically better results than anything previously published would be rejected from top journals and conferences, just because they used a neural network. Yann Lecun's work on convolutional neural networks, which we will study in the next section, showed that these models could read hand-written text--something that had never been achieved before. However his breakthrough was ignored by most researchers, even as it was used commercially to read 10% of the checks in the US!\n",
+ "\n",
+ "In addition to these three Turing Award winners, there are many other researchers who have battled to get us to where we are today. For instance, Jurgen Schmidhuber (who many believe should have shared in the Turing Award) pioneered many important ideas, including working on the *LSTM* architecture with his student Sepp Hochreiter (widely used for speech recognition and other text modeling tasks, and used in the IMDb example in <>). Perhaps most important of all, Werbos invented back-propagation for neural networks, the technique shown in this chapter and used universally for training neural networks. His development was almost entirely ignored for decades, but today it is the most important foundation of modern AI.\n",
+ "\n",
+ "There is a lesson here for all of us! On your deep learning journey you will face many obstacles, both technical, and (even more difficult) people around you who don't believe you'll be successful. There's one *guaranteed* way to fail, and that's to stop trying. We've seen that the only consistent trait amongst every fast.ai student that's gone on to be a world-class practitioner is that they are all very tenacious."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For this initial tutorial we are just going to try to create a model that can recognise \"3\"s and \"7\"s. So let's download a sample of MNIST which contains images of just these digits:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = untar_data(URLs.MNIST_SAMPLE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see what's in this directory by using `ls()`, a method added by fastai. This method returns an object of a special fastai class called `L`, which has all the same functionality of Python's builtin `list`, plus a lot more. One of its handy features is that, when printed, it displays the count of items, before listing the items themselves (if there's more than 10 items, it just shows the first few)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#9) [Path('cleaned.csv'),Path('item_list.txt'),Path('trained_model.pkl'),Path('models'),Path('valid'),Path('labels.csv'),Path('export.pkl'),Path('history.csv'),Path('train')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The MNIST dataset shows is a very common layout for machine learning datasets: separate folders for the *training set*, which is used to train a model, and the *validation set* (and/or *test set*), which is used to evaluate the model (we'll be talking a lot of these concepts very soon!) Let's see what's inside the training set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#2) [Path('train/7'),Path('train/3')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(path/'train').ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There's a folder of \"3\"s, and a folder of \"7\"s. In machine learning parlance, we say that \"3\" and \"7\" are the *labels* in this dataset. Let's take a look in one of these folders (using `sorted` to ensure we all get the same order of files):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#6131) [Path('train/3/10.png'),Path('train/3/10000.png'),Path('train/3/10011.png'),Path('train/3/10031.png'),Path('train/3/10034.png'),Path('train/3/10042.png'),Path('train/3/10052.png'),Path('train/3/1007.png'),Path('train/3/10074.png'),Path('train/3/10091.png')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "threes = (path/'train'/'3').ls().sorted()\n",
+ "sevens = (path/'train'/'7').ls().sorted()\n",
+ "threes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we might expect, it's full of image files. Let’s take a look at one now. Here’s an image of a handwritten number ‘3’, taken from the famous MNIST dataset of handwritten numbers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA9ElEQVR4nM3Or0sDcRjH8c/pgrfBVBjCgibThiKIyTWbWF1bORhGwxARxH/AbtW0JoIGwzXRYhJhtuFY2q1ocLgbe3sGReTuuWbwkx6+r+/zQ/pncX6q+YOldSe6nG3dn8U/rTQ70L8FCGJUewvxl7NTmezNb8xIkvKugr1HSeMP6SrWOVkoTEuSyh0Gm2n3hQyObMnXnxkempRrvgD+gokzwxFAr7U7YXHZ8x4A/Dl7rbu6D2yl3etcw/F3nZgfRVI7rXM7hMUUqzzBec427x26rkmlkzEEa4nnRqnSOH2F0UUx0ePzlbuqMXAHgN6GY9if5xP8dmtHFfwjuQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im3_path = threes[1]\n",
+ "im3 = Image.open(im3_path)\n",
+ "im3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we are using the `Image` class from the *Python Imaging Library* (PIL), which is the most widely used Python package for opening, manipulating, and viewing images. Jupyter knows about PIL images, so it displays the image for us automatically.\n",
+ "\n",
+ "In a computer, everything is represented as a number. To view the numbers that make up this image, we have to convert it to a *NumPy array* or a *PyTorch tensor*. For instance, here's a few numbers from the top-left of the image, converted to a numpy array:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0, 0, 0, 0, 0, 0],\n",
+ " [ 0, 0, 0, 0, 0, 29],\n",
+ " [ 0, 0, 0, 48, 166, 224],\n",
+ " [ 0, 93, 244, 249, 253, 187],\n",
+ " [ 0, 107, 253, 253, 230, 48],\n",
+ " [ 0, 3, 20, 20, 15, 0]], dtype=uint8)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "array(im3)[4:10,4:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and the same thing as a PyTorch tensor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[ 0, 0, 0, 0, 0, 0],\n",
+ " [ 0, 0, 0, 0, 0, 29],\n",
+ " [ 0, 0, 0, 48, 166, 224],\n",
+ " [ 0, 93, 244, 249, 253, 187],\n",
+ " [ 0, 107, 253, 253, 230, 48],\n",
+ " [ 0, 3, 20, 20, 15, 0]], dtype=torch.uint8)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor(im3)[4:10,4:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can slice the array to pick just a part with the top of the digit in it, and then use a Pandas DataFrame to color-code the values using a gradient, which shows us clearly how the image is created from the pixel values:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "im3_t = tensor(im3)\n",
+ "df = pd.DataFrame(im3_t[4:15,4:22])\n",
+ "df.style.set_properties(**{'font-size':'6pt'}).background_gradient('Greys')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"images/att_00058.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see that the background white pixels are stored as the number zero, black is the number 255, and shades of grey are between the two. This image contains 28 pixels across and 28 pixels down, for a total of 768 pixels. (This is much smaller than an image that you would get from a phone camera, which has millions of pixels, but is a convenient size for our initial learning and experiments. We will build up to bigger, full-colour images soon.)\n",
+ "\n",
+ "So, now you've seen what an image looks like to a computer, let's recall our goal: create a model that can recognise “3”s and “7”s. How might you go about getting a computer to do that?\n",
+ "\n",
+ "> stop: Before you read on, take a moment to think about how a computer might be able to recognize these two different digits. What kind of features might it be able to look at? How might it be able to identify these features? How could it combine them together? Learning works best when you try to solve problems yourself, rather than just reading somebody else's answers; so step away from this book for a few minutes, grab a piece of paper and pen, and jot some ideas down…"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## First try: pixel similarity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, here is a first idea: how about we find the average pixel value for every pixel of the threes and do the same for each of the sevens. Then, to classify a digit see which of these two group averages it is most similar to. This certainly seems like it should be better than nothing, so it will make a good baseline."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: A _baseline_ is a simple model which you are confident should perform reasonably well. It should be very simple to implement, and very easy to test, so that you can then test each of your improved ideas, and make sure they are always better than your baseline. Without starting with a sensible baseline, it is very difficult to know whether your super fancy models are actually any good. One good approach to creating a baseline is doing what we have done here: think of a simple, easy to implement model. Another good approach is to search around to find other people that have solved similar problems to yours, and download and run their code on your dataset. Ideally, try both of these!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Step one for our simple model is to get the average of pixel values for each of our two groups. In the process of doing this, we will learn a lot of neat Python numeric programming tricks!\n",
+ "\n",
+ "Let's create a tensor containing all of our threes stacked together. We already know how to create a tensor containing a single image. To create a tensor for every image in a directory, we can use a list comprehension. (Notice also that we use Jupyter to do some little checks of our work along the way; in this case, making sure that the number of returned items seems reasonable):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(6131, 6265)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "seven_tensors = [tensor(Image.open(o)) for o in sevens]\n",
+ "three_tensors = [tensor(Image.open(o)) for o in threes]\n",
+ "len(three_tensors),len(seven_tensors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: List and dictionary comprehensions are a wonderful feature of Python. Many Python programmers use them every day, including all of the authors of this book—they are part of \"idiomatic Python\". But programmers coming from other languages may have never seen them before. There are a lot of great tutorials just a web search away, so we won't spend a long time discussing them now. Here is a quick explanation and example to get you started. A list comprehension looks like this: `new_list = [f(o) for o in a_list if o>0]`. This would return every element of `a_list` that is greater than zero, after passing it to the function `f`. There are three parts here: the collection you are iterating over (`a_list`), an optional filter (`if o>0`), and something to do to each element (`f(o)`). It's not only shorter to write but way faster than the alternative ways of creating the same list with a loop."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll also check that one of the images looks okay. Since we now have tensors (which Jupyter by default will print as values), rather than PIL images (which Jupyter by default will display as an image), we need to use fastai's show_image function to display it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEQAAABECAYAAAA4E5OyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAADjElEQVR4nO2aPyh9YRjHP/f4k38L5X+ysohsUpTBhEVMJGUyGAwWg0kGkcFqlMFIyv+kSGIwKWUiUvKn5P/9DXrvcR+He+695957+vV8llPnvvd9n77n2/s8z3tOIBgMothYqQ7Ab6ggAhVEoIIIVBBBeoTf/+cUFHC6qQ4RqCACFUSggghUEIEKIlBBBCqIQAURRKpUPeHh4QGAyclJAI6PjwFYXl4GIBgMEgh8FY59fX0A3N7eAlBTUwNAU1MTAC0tLQmNVR0iCEQ4MYupl7m4uABgYmICgJWVFQDOz8/DxhUVFQFQX18fGvMbxcXFAFxeXsYSkhPay7jBkz1ke3sbgLa2NgBeX18BeH9/B6CzsxOAnZ0dAAoLCwFC+4ZlWXx8fISNXVpa8iK0qFGHCDxxyN3dHQBPT09h98vLywGYmpoCoKys7Nc5LMsKu0p6enrijtMN6hCBJ1nm8/MTgOfn57D75mlnZWVFnOPq6gqAxsZGwM5I2dnZAOzu7gJQW1vrJiQ3aJZxgyd7iHFCTk5OzHNUVlYCdmYyzjDVrYfO+BN1iCApvYzk5eUFgM3NTQCGhoZCzsjMzARgenoagIGBgaTGpg4RJMUhpnIdHh4GYH5+HrDrl++0t7cD0NXVlYzQfqAOESSk25WY+iQ/Px8g1LeYqxMlJSUAlJaWAjAyMgLYvY7pg+LAcYKkCCIxRdjJyUno3tjYGAD7+/t//tcIMjc3B0Bubm6sYWhh5oaUOMSJt7c3wHaPScn9/f2O4w8PDwGoq6uLdUl1iBtSUpg5kZGRAUBFRQUAvb29AKyurgKwsLAQNn5tbQ2IyyGOqEMEvnGIxKTV39JrdXV1QtZVhwh8k2Uke3t7ADQ3NwP2sYDh5uYGgIKCgliX0CzjBt/tIWdnZwAMDg4CP51h6pK8vLyErK8OEfhmDzF1RUdHB2AfIhnMEePp6Slg1y1xoHuIG1K6h1xfXwMwOzvL+Pg48PVpxHfMS+6trS3AE2f8iTpE4KlDzBPf2NgA7I9bHh8fATg4OADg6OgIsM807u/vQ3OkpaUB9qvLmZkZIHFZRaIOEXiaZbq7uwFYXFyMOpDW1lYARkdHAWhoaIh6jijRLOMGTx1iPnIxtUQkzEHy+vo6VVVVXwHFf3jsFnWIG3xTqaYAdYgbVBCBCiJQQQQqiCBSL5O0osAvqEMEKohABRGoIAIVRKCCCP4B/PMI7HrW9/wAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that amongst our *black bears* is an image that contain two bears, one grizzly, one black. So we should choose `\n",
+ "\n",
+ "Now we can grab the image:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "# For the book, we can't actually click an upload button, so we fake it\n",
+ "btn_upload = SimpleNamespace(data = ['images/grizzly.jpg'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = PILImage.create(btn_upload.data[-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use an `Output` widget to display it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide_output\n",
+ "out_pl = widgets.Output()\n",
+ "out_pl.clear_output()\n",
+ "with out_pl: display(img.to_thumb(128,128))\n",
+ "out_pl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "Then we can get our predictions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Turning your notebook into a real app"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have everything working in this Jupyter notebook, we can create our application. To do this, create a notebook which contains only the code needed to create and show the widgets that you need, and markdown for any text that you want to appear. Have a look at the *bear_classifier* notebook in the book repo to see the simple notebook application we created.\n",
+ "\n",
+ "Next, install Voila if you have not already, by copying these lines into a Notebook cell, and executing it:\n",
+ "\n",
+ " !pip install voila\n",
+ " !jupyter serverextension enable voila --sys-prefix\n",
+ "\n",
+ "Cells which begin with a `!` do not contain Python code, but instead contain code which is passed to your shell, such as bash, power shell in windows, or so forth. If you are comfortable using the command line (which we'll be learning about later in this book), you can of course simply type these two lines (without the `!` prefix) directly into your terminal. In this case, the first line installs the voila library and application, and the second connects it to your existing Jupyter notebook.\n",
+ "\n",
+ "Voila runs Jupyter notebooks, just like the Jupyter notebook server you are using now does, except that it does something very important: it removes all of the cell inputs, and only shows output (including ipywidgets), along with your markdown cells. So what's left is a web application! To view your notebook as a voila web application replace the word \"notebooks\" in your browser's URL with: \"voila/render\". You will see the same content as your notebook, but without any of the code cells.\n",
+ "\n",
+ "Of course, you don't need to use Voila or ipywidgets. Your model is just a function you can call: `pred,pred_idx,probs = learn.predict(img)` . So you can use it with any framework, hosted on any platform. And you can take something you've prototyped in ipywidgets and Voila and later convert it into a regular web application. We're showing you this approach in the book because we think it's a great way for data scientists and other folks that aren't web development experts to create applications from their models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Deploying your app"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we now know, you need a GPU to train nearly any useful deep learning model. So, do you need a GPU to use that model in production? No! You almost certainly **do not need a GPU to serve your model in production**. There's a few reasons for this:\n",
+ "\n",
+ "- As we've seen, GPUs are only useful when they do lots of identical work in parallel. If you're doing (say) image classification, then you'll normally be classifying just one user's image at a time, and there isn't normally enough work to do in a single image to keep a GPU busy for long enough for it to be very efficient. So a CPU will often be more cost effective.\n",
+ "- An alternative could be to wait for a few users to submit their images, and then batch them up, and do them all at once on a GPU. But then you're asking your users to wait, rather than getting answers straight away! And you need a high volume site for this to be workable.\n",
+ "- The complexities of dealing with GPU inference are significant. In particular, the GPU's memory will need careful manual management, and you'll need some careful queueing system to ensure you only do one batch at a time\n",
+ "- There's a lot more market competition in CPU servers than GPU, as a result of which there's much cheaper options available for CPU servers.\n",
+ "\n",
+ "Because of the complexity of GPU serving, many systems have sprung up to try to automate this. However, managing and running these systems is themselves complex, and generally requires compiling your model into a different form that's specialized for that system. It doesn't make sense to deal with this complexity until/unless your app gets popular enough that it makes clear financial sense for you to do so."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For at least the initial prototype of your application, and for any hobby projects that you want to show off, you can easily host them for free. The best place and the best way to do this will vary over time so check the book website for the most up-to-date recommendations. As we're writing this book in 2020 the simplest (and free!) approach is called [Binder](https://mybinder.org/). To publish your web app on Binder, you follow these steps:\n",
+ "\n",
+ "1. Add your notebook to a [GitHub repository](http://github.com/), \n",
+ "2. Paste the URL of that repo in the URL field of Binder, \n",
+ "3. Change the \"File\" dropdown to instead select \"URL\",\n",
+ "4. In the Path field, enter `/voila/render/name.ipynb` (replacing `name.ipynb` as appropriate for your notebook):\n",
+ "5. Click the \"Copy the URL\" button and paste it somewhere safe. \n",
+ "6. Click \"Launch\"."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first time you do this Binder will take around 5 minutes to build your site. In other words, is it finding a virtual machine which can run your app, allocating storage, collecting the files needed for Jupyter, for your notebook, and for presenting your notebook as a web application. It's doing all of this behind the scenes.\n",
+ "\n",
+ "Finally, once it has started the app running, it will navigate your browser to your new web app. You can share the URL you copied to allow others to access your app as well.\n",
+ "\n",
+ "For other (both free and paid) options for deploying your web app, be sure to take a look at the book web site."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You may well want to deploy your application onto mobile devices, or edge devices such as a Raspberry Pi. There are a lot of libraries and frameworks to allow you to integrate a model directly into a mobile application. However these approaches tend to require a lot of extra steps and boilerplate, and do not always support all the PyTorch and fastai layers that your model might use. In addition, the work you do will depend on what kind of mobile devices you are targeting for deployment. So you might need to do some work to run on iOS devices, different work to run on newer Android devices, different work for older Android devices, etc.. Instead, we recommend wherever possible that you deploy the model itself to a server, and have your mobile or edge application connect to it as a web service.\n",
+ "\n",
+ "There is quite a few upsides to this approach. The initial installation is easier, because you only have to deploy a small GUI application, which connects to the server to do all the heavy lifting. More importantly perhaps, upgrades of that core logic can happen on your server, rather than needing to be distributed to all of your users. Your server can have a lot more memory and processing capacity than most edge devices, and it is far easier to scale those resources if your model becomes more demanding. The hardware that you will have on a server is going to be more standard and more easily supported by fastai and PyTorch, so you don't have to compile your model into a different form.\n",
+ "\n",
+ "There are downsides too, of course. Your application will require a network connection, and there will be some latency each time the model is called. It takes a while for a neural network model to run anyway, so this additional network latency may not make a big difference to your users in practice. In fact, since you can use better hardware on the server, the overall latency may even be less! If your application uses sensitive data then your users may be concerned about an approach which sends that data to a remote server, so sometimes privacy considerations will mean that you need to run the model on the edge device. Sometimes this can be avoided by having a *on premise* server, such as inside a company's firewall. Managing the complexity and scaling the server can create additional overhead, whereas if your model runs on the edge devices then each user is bringing their own compute resources, which leads to easier scaling with an increasing number of users (also known as _horizontal scaling_)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> A: I've had a chance to see up close how the mobile ML landscape is changing in my work. We offer an iPhone app that depends on computer vision and for years we ran our own computer vision models in the cloud. This was the only way to do it then since those models needed significant memory and compute resources and took minutes to process. This approach required building not only the models (fun!) but infrastructure to ensure a certain number of \"compute worker machines\" was absolutely always running (scary), that more machines would automatically come online if traffic increased, that there was stable storage for large inputs and outputs, that the iOS app could know and tell the user how their job was doing, etc... Nowadays, Apple provides APIs for converting models to run efficiently on device and most iOS devices have dedicated ML hardware, so we run our new models on device. So, in a few years that strategy has gone from impossible to possible but it's still not easy. In our case it's worth it, for a faster user experiene and to worry less about servers. What works for you will depend, realistically, on the user experience you're trying to create and what you personally find it easy to do. If you really know how to run servers, do it. If you really know how to build native mobile apps, do that. There are many roads up the hill.\n",
+ "\n",
+ "Overall, we'd recommend using a simple CPU-based server approach where possible, for as long as you can get away with it. If you're lucky enough to have a very successful application, then you'll be able to justify the investment in more complex deployment approaches at that time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How to avoid disaster"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In practice, a deep learning model will be just one piece of a much bigger system. As we discussed at the start of this chapter, a *data product* requires thinking about the entire end to end process within which or model lives.\n",
+ "\n",
+ "One of the biggest issues with this is that understanding and testing the behavior of a deep learning model is much more difficult than most code that you would write. With normal software development you can analyse the exact steps that the software is taking, and carefully study with of these steps match the desired behaviour that you are trying to create. But with a neural network the behavior emerges from the models attempt to match the training data, rather than being exactly defined.\n",
+ "\n",
+ "This can result in disaster! For instance, let's say you really were rolling out a bear detection system which will be attached to video cameras around the campsite, and will warn campers of incoming bears. If we used a model trained with the dataset we downloaded, there are going to be all kinds of problems in practice, such as:\n",
+ "\n",
+ "- working with video data instead of images ;\n",
+ "- handling nighttime images, which may not appear in this dataset ;\n",
+ "- dealing with low resolution camera images ;\n",
+ "- ensuring results are returned fast enough to be useful in practice ;\n",
+ "- recognising bears in positions that are rarely seen in photos that people post online (for example from behind, partially covered by bushes, or when a long way away from the camera)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A big part of the issue is that the kinds of photos that people are most likely to upload to the Internet are the kinds of photos that do a good job of clearly and artistically displaying their subject matter. So we may need to do a lot of our own data collection and labelling to create a useful system.\n",
+ "\n",
+ "This is just one example of the more general problem of *out of domain* data. That is to say, there may be data that our model sees in production which is very different to what it saw during training. There isn't really a complete technical solution to this problem; instead we have to be careful about our approach to rolling out the technology.\n",
+ "\n",
+ "There are other reasons we need to be careful too. One very common problem is *domain shift*; this is where the type of data that our model sees changes over time. For instance, an insurance company may use a deep learning model as part of their pricing and risk algorithm, but over time the type of customers that they attract, and the type of risks that they represent, may change so much that the original training data is no longer relevant.\n",
+ "\n",
+ "Out of domain data, and domain shift, are examples of the problem that you can never fully no the entire behaviour of your neural network. They have far too many parameters to be able to analytically understand all of their possible behaviours. This is the natural downside of the thing that they're so good at — their flexibility in being able to solve complex problems where we may not even be able to fully specify our preferred solution approaches. The good news, however, is that there are ways to mitigate these risks using a carefully thought out process. The details of this will vary depending on the details of the problem you are solving, but we will attempt to lay out here a high-level approach which we hope will provide useful guidance."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Where possible, the first step is to use an entirely manual process, with your deep learning model approach running in parallel, but not being used directly to drive any actions. The humans involved in the manual process should look at the deep learning outputs and check whether they make sense. For instance, with our bear classifier a park ranger could have a screen displaying any time a possible bear sighting occurred in any camera, and simply highlight them in red on the screen. The park ranger would still be expected to be just as alert as before the model was deployed; they are simply helping to check for problems at this point.\n",
+ "\n",
+ "The second step is to try to limit the scope of the model, and have it carefully supervised by people. For instance, do a small geographically and time constrained trial of the model-driven approach. Rather than rolling your bear classifier out in every national park throughout the country, pick a single observation post, for a one-week period, and have a park ranger check each alert before it goes out.\n",
+ "\n",
+ "Then, gradually increase the scope of your rollout. As you do so, ensure that you have really good reporting systems in place, to make sure that you are aware of any significant changes to the actions being taken compared to your manual process. For instance, if the number of bear alerts doubles or halves after rollout of the new system in some location we should be very concerned. Try to think about all the ways in which your system could go wrong, and then think about what measure or report or picture could reflect that problem, and then ensure that your regular reporting includes that information."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: I started a company 20 years ago called *Optimal Decisions* which used machine learning and optimisation to help giant insurance companies set their pricing, impacting tens of billions of dollars of risks. We used the approaches described above to manage the potential downsides of something that might go wrong. Also, before we worked with our clients to put anything in production, we tried to simulate the impact by testing the end to end system on their previous year's data. It was always quite a nerve-wracking process, putting these new algorithms in production, but every rollout was successful."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Unforeseen consequences and feedback loops"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the biggest challenges in rolling out a model is that your model may change the behaviour of the system it is a part of. For instance, consider YouTube's recommendation system. A couple of years ago Google talked about how they had introduced reinforcement learning (closely related to deep learning, but where your loss function represents a result which could be a long time after an action occurs) to improve their recommendation system. They described how they used an algorithm which made recommendations such that watch time would be optimised.\n",
+ "\n",
+ "However, human beings tend to be drawn towards controversial content. This meant that videos about wings like conspiracy theories started to get recommended more and more by the recommendation system. Furthermore, it turns out that the kinds of people that are interested in conspiracy theories are also people that watch a lot of online videos! So, they started to get drawn more and more towards YouTube. The increasing number of conspiracy theorists watching YouTube resulted in the algorithm recommending more and more conspiracy theories and other extremist content, which resulted in more extremists watching videos on YouTube, and more people watching YouTube developing extremist views, which led to the algorithm recommending more extremist content... The system became so out of control that in February 2019 it led the New York Times to run the headline \"YouTube Unleashed a Conspiracy Theory Boom. Can It Be Contained?\"\n",
+ "\n",
+ "A helpful exercise prior to rolling out a significant machine learning system is to consider this question: \"what would happen if it went really, really well?\" In other words, what if the predictive power was extremely high, and its ability to influence behaviour was extremely significant? In that case, who would be most impacted? What would the most extreme results potentially look like? How would you know what was really going on?\n",
+ "\n",
+ "Such a thought exercise might help you to construct a more careful rollout plan, ongoing monitoring systems, and human oversight. Of course, human oversight isn't useful if it isn't listened to; so make sure that there are reliable and resilient communication channels so that the right people will be aware of issues, and will have the power to fix them."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get writing!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the things our students have found most helpful to solidify their understanding of this material is to write it down. There is no better test of your understanding of a topic than attempting to teach it to somebody else. This is helpful even if you never show your writing to anybody — but it's even better if you share it! So we recommend that, if you haven't already, you start a blog. Now that you've finished chapter 2, and have learned how to train and deploy models, you're well placed to write your first blog post about your deep learning journey. What's surprised you? What opportunities do you see for deep learning in your field? What obstacles do you see?\n",
+ "\n",
+ "Rachel Thomas, co-founder of fast.ai, wrote in the article [Why you (yes, you) should blog](https://medium.com/@racheltho/why-you-yes-you-should-blog-7d2544ac1045):\n",
+ "\n",
+ "```asciidoc\n",
+ "____\n",
+ "The top advice I would give my younger self would be to start blogging sooner. Here are some reasons to blog:\n",
+ "\n",
+ "* It’s like a resume, only better. I know of a few people who have had blog posts lead to job offers!\n",
+ "* Helps you learn. Organizing knowledge always helps me synthesize my own ideas. One of the tests of whether you understand something is whether you can explain it to someone else. A blog post is a great way to do that.\n",
+ "* I’ve gotten invitations to conferences and invitations to speak from my blog posts. I was invited to the TensorFlow Dev Summit (which was awesome!) for writing a blog post about how I don’t like TensorFlow.\n",
+ "* Meet new people. I’ve met several people who have responded to blog posts I wrote.\n",
+ "* Saves time. Any time you answer a question multiple times through email, you should turn it into a blog post, which makes it easier for you to share the next time someone asks.\n",
+ "____\n",
+ "```\n",
+ "\n",
+ "Perhaps her most important tip is this: \"*You are best positioned to help people one step behind you. The material is still fresh in your mind. Many experts have forgotten what it was like to be a beginner (or an intermediate) and have forgotten why the topic is hard to understand when you first hear it. The context of your particular background, your particular style, and your knowledge level will give a different twist to what you’re writing about*.\"\n",
+ "\n",
+ "We've provided full details on how to set up a blog in an appendix \"_Creating a blog_\". If you don't have a blog already, jump over to that chapter now, because we've got a really great approach set up for you to start blogging, for free, with no ads--and you can even use Jupyter Notebook!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Provide an example of where the bear classification model might work poorly, due to structural or style differences to the training data\n",
+ "1. Where do text models currently have a major deficiency?\n",
+ "1. What are possible negative societal implications of text generation models?\n",
+ "1. In situations where a model might make mistakes, and those mistakes could be harmful, what is a good alternative to automating a process?\n",
+ "1. What kind of tabular data is deep learning particularly good at?\n",
+ "1. What's a key downside of directly using a deep learning model for recommendation systems?\n",
+ "1. What are the steps of the Drivetrain approach?\n",
+ "1. How do the steps of the Drivetrain approach map to a recommendation system?\n",
+ "1. Create an image recognition model using data you curate, and deploy it on the web.\n",
+ "1. What is `DataLoaders`?\n",
+ "1. What four things do we need to tell fastai to create `DataLoaders`?\n",
+ "1. What does the `splitter` parameter to `DataBlock` do?\n",
+ "1. How do we ensure a random split always gives the same validation set?\n",
+ "1. What letters are often used to signify the independent and dependent variables?\n",
+ "1. What's the difference between crop, pad, and squish resize approaches? When might you choose one over the other?\n",
+ "1. What is data augmentation? Why is it needed?\n",
+ "1. What is the difference between `item_tfms` and `batch_tfms`?\n",
+ "1. What is a confusion matrix?\n",
+ "1. What does `export` save?\n",
+ "1. What is it called when we use a model for getting predictions, instead of training?\n",
+ "1. What are IPython widgets?\n",
+ "1. When might you want to use CPU for deployment? When might GPU be better?\n",
+ "1. What are the downsides of deploying your app to a server, instead of to a client (or edge) device such as a phone or PC?\n",
+ "1. What are 3 examples of problems that could occur when rolling out a bear warning system in practice?\n",
+ "1. What is \"out of domain data\"?\n",
+ "1. What is \"domain shift\"?\n",
+ "1. What are the 3 steps in the deployment process?\n",
+ "1. For a project you're interested in applying deep learning to, consider the thought experiment \"what would happen if it went really, really well?\"\n",
+ "1. Start a blog, and write your first blog post. For instance, write about what you think deep learning might be useful for in a domain you're interested in."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Consider how the Drivetrain approach maps to a project or problem you're interested in.\n",
+ "1. When might it be best to avoid certain types of data augmentation?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/03_ethics.ipynb b/03_ethics.ipynb
new file mode 100644
index 000000000..06e51edfb
--- /dev/null
+++ b/03_ethics.ipynb
@@ -0,0 +1,988 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_ethics]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data Ethics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Acknowledgement: Dr Rachel Thomas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This chapter was co-authored by Dr Rachel Thomas, the co-founder of fast.ai, and founding director of the Center for Applied Data Ethics at the University of San Francisco. It largely follows a subset of her syllabus for the \"Introduction to Data Ethics\" course that she developed."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introduction to data ethics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we discussed in chapters 1 and 2, sometimes, machine learning models can go wrong. They can have bugs. They can be presented with data that they haven't seen before, and behave in ways we don't expect. Or, they could work exactly as designed, but be used for something that you would much prefer they were never ever used for.\n",
+ "\n",
+ "Because deep learning is such a powerful tool and can be used for so many things, it becomes particularly important that we consider the consequences of our choices. The philosophical study of *ethics* is the study of right and wrong, including how we can define those terms, recognise right and wrong actions, and understand the connection between actions and consequences. The field of *data ethics* has been around for a long time, and there are many academics focused on this field. It is being used to help define policy in many jurisdictions; it is being used in companies big and small to consider how best to ensure good societal outcomes from product development; and it is being used by researchers who want to make sure that the work they are doing is used for good, and not for bad.\n",
+ "\n",
+ "As a deep learning practitioner, therefore, it is likely that at some point you are going to be put in a situation where you need to consider data ethics. So what is data ethics? It's a subfield of ethics, so let's start there."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: At university, philosophy of ethics was my main thing (it would have been the topic of my thesis, if I'd finished it, instead of dropping out to join the real-world). Based on the years I spent studying ethics, I can tell you this: no one really agrees on what right and wrong are, whether they exist, how to spot them, which people are good, and which bad, or pretty much anything else. So don't expect too much from the theory! We're going to focus on examples and thoughts starters here, not theory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In answering the question [What is Ethics](https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/what-is-ethics/), The Markkula Center for Applied Ethics says that *ethics* refers to:\n",
+ "\n",
+ "- Well-founded standards of right and wrong that prescribe what humans ought to do, and\n",
+ "- The study and development of one's ethical standards.\n",
+ "\n",
+ "There is no list of right answers for ethics. There is no list of dos and don'ts. Ethics is complicated, and context-dependent. It involves the perspectives of many stakeholders. Ethics is a muscle that you have to develop and practice. In this chapter, our goal is to provide some signposts to help you on that journey.\n",
+ "\n",
+ "Spotting ethical issues is best to do as part of a collaborative team. This is the only way you can really incorporate different perspectives. Different people's backgrounds will help them to see things which may not be obvious to you. Working with a team is helpful for many \"muscle building\" activities, including this one.\n",
+ "\n",
+ "This chapter is certainly not the only part of the book where we talk about data ethics, but it's good to have a place where we focus on it for a while. To get oriented, it's perhaps easiest to look at a few examples. So we picked out three that we think illustrate effectively some of the key topics."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting started with some examples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are going to start with three specific examples that illustrate three common ethical issues in tech:\n",
+ "\n",
+ "1. **Recourse processes**: Arkansas's buggy healthcare algorithms left patients stranded\n",
+ "2. **Feedback loops**: YouTube's recommendation system helped unleash a conspiracy theory boom\n",
+ "3. **Bias**: When a traditionally African-American name is searched for on Google, it displays ads for criminal background checks.\n",
+ "\n",
+ "In fact, for every concept that we introduce in this chapter, we are going to provide at least one specific example. For each one, have a think about what you could have done in this situation, and think about what kinds of obstructions there might have been to you getting that done. How would you deal with them? What would you look out for?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Bugs and recourse: Buggy algorithm used for healthcare benefits"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Verge investigated software used in over half of U.S. states to determine how much healthcare people receive, and documented their findings in an article [What Happens When an Algorithm Cuts Your Healthcare](https://www.theverge.com/2018/3/21/17144260/healthcare-medicaid-algorithm-arkansas-cerebral-palsy). After implementation of the algorithm in Arkansas, people (many with severe disabilities) drastically had their healthcare cut. For instance, Tammy Dobbs, a woman with cerebral palsy who needs an aid to help her to get out of bed, to go to the bathroom, to get food, and more, had her hours of help suddenly reduced by 20 hours a week. She couldn’t get any explanation for why her healthcare was cut. Eventually, a court case revealed that there were mistakes in the software implementation of the algorithm, negatively impacting people with diabetes or cerebral palsy. However, Dobbs and many other people reliant on these health care benefits live in fear that their benefits could again be cut suddenly and inexplicably."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Feedback loops: YouTube's recommendation system"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Feedback loops can occur when your model is controlling the next round of data you get. The data that is returned quickly becomes flawed by the software itself.\n",
+ "\n",
+ "For instance, in <"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Being a computer scientist, she studied this systematically, and looked at over 2000 names. She found that this pattern held where historically black names received advertisements suggesting that the person had a criminal record. Whereas, white names had more neutral advertisements.\n",
+ "\n",
+ "This is an example of bias. It can make a big difference to people's lives — for instance, if a job applicant is googled that it may appear that they have a criminal record when they do not."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## So what?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One very natural reaction to considering these issues is: \"So what? What's that got to do with me? I'm a data scientist, not a politician. I'm not the senior executive at my company who make the decisions about what we do. I'm just trying to build the most predictive model I can.\"\n",
+ "\n",
+ "These are very reasonable questions. But we're going to try to convince you that the answer is: everybody who is training models absolutely needs to consider how their model will be used. And to consider how to best ensure that it is used as positively as possible. There are things you can do. And if you don't do these things, then things can go pretty bad.\n",
+ "\n",
+ "One particularly hideous example of what happens when technologists focus on technology at all costs is the story of IBM and Nazi Germany. A Swiss judge ruled \"It does not thus seem unreasonable to deduce that IBM's technical assistance facilitated the tasks of the Nazis in the commission of their crimes against humanity, acts also involving accountancy and classification by IBM machines and utilized in the concentration camps themselves.\"\n",
+ "\n",
+ "IBM, you see, supplied the Nazis with data tabulation products necessary to track the extermination of Jews and other groups on a massive scale. This was driven from the top of the company, with marketing to Hitler and his leadership team. Company President Thomas Watson personally approved the 1939 release of special IBM alphabetizing machines to help organize the deportation of Polish Jews. Pictured here is Adolf Hitler (far left) meeting with IBM CEO Tom Watson Sr. (2nd from left), shortly before Hitler awarded Watson a special “Service to the Reich” medal in 1937:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But it also happened throughout the organization. IBM and its subsidiaries provided regular training and maintenance on-site at the concentration camps: printing off cards, configuring machines, and repairing them as they broke frequently. IBM set up categorizations on their punch card system for the way that each person was killed, which group they were assigned to, and the logistical information necessary to track them through the vast Holocaust system. IBM's code for Jews in the concentration camps was 8, where around 6,000,000 were killed. Its code for Romanis was 12 (they were labeled by the Nazis as \"asocials\", with over 300,000 killed in the *Zigeunerlager*, or “Gypsy camp”). General executions were coded as 4, death in the gas chambers as 6."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Of course, the project managers and engineers and technicians involved were just living their ordinary lives. Caring for their families, going to the church on Sunday, doing their jobs as best as they could. Following orders. The marketers were just doing what they could to meet their business development goals. Edwin Black, author of \"IBM and the Holocaust\", said: \"To the blind technocrat, the means were more important than the ends. The destruction of the Jewish people became even less important because the invigorating nature of IBM's technical achievement was only heightened by the fantastical profits to be made at a time when bread lines stretched across the world.\"\n",
+ "\n",
+ "Step back for a moment and consider: how would you feel if you discovered that you had been part of a system that ending up hurting society? Would you even know? Would you be open to finding out? How can you help make sure this doesn't happen? We have described the most extreme situation here in Nazi Germany, but there are many negative societal consequences happening due to AI and machine learning right now, some of which we'll describe in this chapter.\n",
+ "\n",
+ "It's not just a moral burden either. Sometimes, technologists very directly pay for their actions. For instance, the first person who was jailed as a result of the Volkswagen scandal, where the car company cheated on their diesel emissions tests, was not the manager that oversaw the project, or an executive at the helm of the company. It was one of the engineers, James Liang, who just did what he was told.\n",
+ "\n",
+ "On the other hand, if a project you are involved in turns out to make a huge positive impact on even one person, this is going to make you feel pretty great!\n",
+ "\n",
+ "Okay, so hopefully we have convinced you that you ought to care. Now the question is: can you actually do anything can you make an impact beyond just maximising the predictive power of your models? Consider the pipeline are steps that occurs between the development of a model or an algorithm by a researcher or practitioner, and the point at which this work is actually used to make some decision. Normally there is a very long chain from one end to the other. This is especially true if you are a researcher where you don't even know if your research will ever get used for anything. It's especially tricky if you're involved in data collection, which is even earlier in the pipeline.\n",
+ "\n",
+ "Data often ends up being used for different purposes than why it was originally collected. IBM began selling to Nazi Germany well before the Holocaust, including helping with Germany’s 1933 census conducted by Adolf Hitler, which was effective at identifying far more Jewish people than had previously been recognized in Germany. US census data was used to round up Japanese-Americans (who were US citizens) for internment during World War II. It is important to recognize how data and images collected can be weaponized later. Columbia professor [Tim Wu wrote](https://www.nytimes.com/2019/04/10/opinion/sunday/privacy-capitalism.html) that “You must assume that any personal data that Facebook or Android keeps are data that governments around the world will try to get or that thieves will try to steal.”"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Integrating machine learning with product design"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Presumably the reason you're doing this work is because you hope it will be used for something. Otherwise, you're just wasting your time. So, let's start with the assumption that your work will end up somewhere. Now, as you are collecting your data and developing your model, you are making lots of decisions. What level of aggregation will you store your data at? What loss function should you use? What validation and training sets should you use? Should you focus on simplicity of implementation, speed of inference, or accuracy of the model? How will your model handle out of domain data items? Can it be fine-tuned, or must it be retrained from scratch over time?\n",
+ "\n",
+ "These are not just algorithm questions. They are data product design questions. But the product managers, executives, judges, journalists, doctors… whoever ends up developing and using the system of which your model is a part will not be well-placed to understand the decisions that you made, let alone change them.\n",
+ "\n",
+ "For instance, two studies found that Amazon’s facial recognition software produced [inaccurate](https://www.nytimes.com/2018/07/26/technology/amazon-aclu-facial-recognition-congress.html) and [racially biased results](https://www.theverge.com/2019/1/25/18197137/amazon-rekognition-facial-recognition-bias-race-gender). Amazon claimed that the researchers should have changed the default parameters. However, it turned out that [Amazon was not instructing police departments](https://gizmodo.com/defense-of-amazons-face-recognition-tool-undermined-by-1832238149) that use its software to do this either. There was, presumably, a big distance between the researchers that developed these algorithms, and the Amazon documentation staff that wrote the guidelines provided to the police. A lack of tight integration led to serious problems for society, the police, and Amazon themselves. It turned out that their system erroneously *matched* 28 members of congress to criminal mugshots! (And these members of congress wrongly matched to criminal mugshots disproportionately included people of color.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data scientists need to be part of a cross disciplinary team. And researchers need to work closely with the kinds of people who will end up using their research. Better still is if the domain experts themselves have learnt enough to be able to train and debug some models themselves — hopefully there's a few of you reading this book right now!\n",
+ "\n",
+ "The modern workplace is a very specialised place. Everybody tends to have very well-defined jobs to perform. Especially in large companies, it can be very hard to know what all the pieces of the puzzle are. Sometimes companies even intentionally obscure the overall project goals that are being worked on, if they know that their employees are not going to like the answers. This is sometimes done by compartmentalising every piece as much as possible\n",
+ "\n",
+ "In other words, we're not saying that any of this is easy. It's hard. It's really hard. We all have to do our best. And with often seen that the people who do get involved in the higher-level context of these projects, and attempt to develop cross disciplinary capabilities and teams, become some of the most important and well rewarded parts of their organisations. It's the kind of work that tends to be highly appreciated by senior executives, even if it is considered, sometimes, rather uncomfortable by middle management."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Topics in Data Ethics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data ethics is a big field, and we can't cover everything. Instead, we're going to pick a few topics which we think are particularly relevant:\n",
+ "- need for recourse and accountability\n",
+ "- feedback loops\n",
+ "- bias\n",
+ "- disinformation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Errors and recourse"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In a complex system it is easy for no one person to feel responsible for outcomes. While this is understandable, it does not lead to good results. In the example above of the Arkansas healthcare system in which a bug led to people with cerebral palsy losing access to needed care, the creator of the algorithm blamed government officials, and government officials could blame those who implemented the software. NYU professor danah boyd described this phenomenon: \"bureaucracy has often been used to evade responsibility, and today's algorithmic systems are extending bureaucracy.\"\n",
+ "\n",
+ "An additional reason why recourse is so necessary, is because data often contains errors. Mechanisms for audits and error-correction are crucial. A database of suspected gang members maintained by California law enforcement officials was found to be full of errors, including 42 babies who had been added to the database when they were less than 1 year old (28 of whom were marked as “admitting to being gang members”). In this case, there was no process in place for correcting mistakes or removing people once they’ve been added. Another example is the US credit report system; in a large-scale study of credit reports by the FTC in 2012, it was found that 26% of consumers had at least one mistake in their files, and 5% had errors that could be devastating. Yet, the process of getting such errors corrected is incredibly slow and opaque. When public-radio reporter Bobby Allyn discovered that he was erroneously listed as having a firearms conviction, it took him \"more than a dozen phone calls, the handiwork of a county court clerk and six weeks to solve the problem. And that was only after I contacted the company’s communications department as a journalist.\" (as covered in the article [How the careless errors of credit reporting agencies are ruining people’s lives](https://www.washingtonpost.com/posteverything/wp/2016/09/08/how-the-careless-errors-of-credit-reporting-agencies-are-ruining-peoples-lives/))\n",
+ "\n",
+ "As machine learning practitioners, we do not always think of it as our responsibility to understand how our algorithms and up being implemented in practice. But we need to."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Feedback loops"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The New York Times published another article on YouTube's recommendation system, titled [On YouTube’s Digital Playground, an Open Gate for Pedophiles](https://www.nytimes.com/2019/06/03/world/americas/youtube-pedophiles.html). The article started with this chilling story:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> : Christiane C. didn’t think anything of it when her 10-year-old daughter and a friend uploaded a video of themselves playing in a backyard pool… A few days later… the video had thousands of views. Before long, it had ticked up to 400,000... “I saw the video again and I got scared by the number of views,” Christiane said. She had reason to be. YouTube’s automated recommendation system… had begun showing the video to users who watched other videos of prepubescent, partially clothed children, a team of researchers has found.\n",
+ "\n",
+ "> : On its own, each video might be perfectly innocent, a home movie, say, made by a child. Any revealing frames are fleeting and appear accidental. But, grouped together, their shared features become unmistakable."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "YouTube's recommendation algorithm had begun curating playlists for pedophiles, picking out innocent home videos that happened to contain prepubescent, partially clothed children. \n",
+ "\n",
+ "No one at Google planned to create a system that turned family videos into porn for pedophiles. So what happened?\n",
+ "\n",
+ "Part of the problem here is the centrality of metrics in driving a financially important system. When an algorithm has a metric to optimise, as you have seen, it will do everything it can to optimise that number. This tends to lead to all kinds of edge cases, and humans interacting with a system will search for, find, and exploit these edge cases and feedback loops for their advantage.\n",
+ "\n",
+ "There are signs that this is exactly what has happened with YouTube's recommendation system. The Guardian ran an article [How an ex-YouTube insider investigated its secret algorithm](https://www.theguardian.com/technology/2018/feb/02/youtube-algorithm-election-clinton-trump-guillaume-chaslot) about Guillaume Chaslot, an ex-YouTube engineer who created AlgoTransparency, which tracks these issues. Chaslot published this chart, following the release of Robert Mueller's \"Report on the Investigation Into Russian Interference in the 2016 Presidential Election\":"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Russia Today's coverage of the Mueller report was an extreme outlier in how many channels were recommending it. This suggests the possibility that Russia Today, a state-owned Russia media outlet, has been successful in gaming YouTube's recommendation algorithm. The lack of transparency of systems like this make it hard to uncover the kinds of problems that we're discussing.\n",
+ "\n",
+ "Another example of a feedbakc loop is a predictive policing algorithm that predicts more crime in certain neighborhoods, causing more police officers to be sent to those neighborhoods, which can result in more crime being recorded in those neighborhoods, and so on. University of Utah computer science processor Suresh Venkatasubramanian says about this: \"Predictive policing is aptly named: it is predicting future policing, not future crime.”\n",
+ "\n",
+ "There are positive examples of people and organizations attempting to combat these problems. Evan Estola, lead machine learning engineer at Meetup, [discussed the example](https://www.youtube.com/watch?v=MqoRzNhrTnQ) of men expressing more interest than women in tech meetups. Meetup’s algorithm could recommend fewer tech meetups to women, and as a result, fewer women would find out about and attend tech meetups, which could cause the algorithm to suggest even fewer tech meetups to women, and so on in a self-reinforcing feedback loop. Evan and his team made the ethical decision for their recommendation algorithm to not create such a feedback loop, but explicitly not using gender for that part of their model. It is encouraging to see a company not just unthinkingly optimize a metric, but to consider their impact. \"You need to decide which feature not to use in your algorithm… the most optimal algorithm is perhaps not the best one to launch into production\", he said.\n",
+ "\n",
+ "While Meetup chose to avoid such an outcome, Facebook provides an example of allowing a runaway feedback loop to run wild. Facebook radicalizes users interested in one conspiracy theory by introducing them to more. As [Renee DiResta, a researcher on proliferation of disinformation, writes](https://www.fastcompany.com/3059742/social-network-algorithms-are-distorting-reality-by-boosting-conspiracy-theories):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> : \"once people join a single conspiracy-minded \\[Facebook\\] group, they are algorithmically routed to a plethora of others. Join an anti-vaccine group, and your suggestions will include anti-GMO, chemtrail watch, flat Earther (yes, really), and ‘curing cancer naturally’ groups. Rather than pulling a user out of the rabbit hole, the recommendation engine pushes them further in.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Discussions of bias online tend to get pretty confusing pretty fast. The word bias mean so many different things. Statisticians often think that when data ethicists are talking about bias that they're talking about the statistical definition of the term bias. But they're not. And they're certainly not talking about the bias is that appear in the weights and bias is which are the parameters of your model!\n",
+ "\n",
+ "What they're talking about is the social science concept of bias. In [A Framework for Understanding Unintended Consequences of Machine Learning](https://arxiv.org/abs/1901.10002) MIT's Suresh and Guttag describe six types of bias in machine learning, summarized in this figure from their paper:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll discuss four of these types of bias here (see the paper for details on the others)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Historical bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Historical bias* comes from the fact that people are biased, processes are biased, and society is biased. Suresh and Guttag say: \"Historical bias is a fundamental, structural issue with the first step of the data generation process and can exist even given perfect sampling and feature selection\".\n",
+ "\n",
+ "For instance, here's a few examples of historical *race bias* in the US, from the NY Times article [Racial Bias, Even When We Have Good Intentions](https://www.nytimes.com/2015/01/04/upshot/the-measuring-sticks-of-racial-bias-.html), by the University of Chicago's Sendhil Mullainathan:\n",
+ "\n",
+ " - When doctors were shown identical files, they were much less likely to recommend cardiac catheterization (a helpful procedure) to Black patients\n",
+ " - When bargaining for a used car, Black people were offered initial prices $700 higher and received far smaller concessions\n",
+ " - Responding to apartment-rental ads on Craigslist with a Black name elicited fewer responses than with a white name\n",
+ " - An all-white jury was 16 points more likely to convict a Black defendant than a white one, but when a jury had 1 Black member, it convicted both at same rate.\n",
+ "\n",
+ "The COMPAS algorithm, widely used for sentencing and bail decisions in the US, is an example of an important algorithm which, when tested by ProPublica, showed clear racial bias in practice:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Any dataset involving humans can have this kind of bias, such as medical data, sales data housing data, political data, and so on. Because underlying bias is so pervasive, bias in datasets is very pervasive. Racial bias even turns up in computer vision, as shown in this example of auto-categorized photos shared on Twitter by a Google Photos user:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Yes, that is showing what you think it is: Google Photos classified a Black user's photo with their friend as \"gorrilas\"! This algorithmic mis-step got a lot of attention in the media. “We’re appalled and genuinely sorry that this happened,” a company spokeswoman said. “There is still clearly a lot of work to do with automatic image labeling, and we’re looking at how we can prevent these types of mistakes from happening in the future.”\n",
+ "\n",
+ "Unfortunately, fixing problems in machine learning systems when the input data has problems is hard. Google's first attempt didn't inspire confidence, as covered by The Guardian:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These kinds of problem are certainly not limited to just Google. MIT researchers studied the most popular online computer vision APIs to see how accurate they were. But they didn't just calculate a single accuracy number—instead, they looked at the accuracy across four different groups:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "IBM's system, for instance, had a 34.7% error rate for darker females, vs 0.3% for lighter males—over 100 times more errors! Some people incorrectly reacted to these experiments by claiming that the difference was simply because darker skin is harder for computers to recognise. However, what actually happened, is after the negative publicity that this result created, all of the companies in question dramatically improved their models for darker skin, such that one year later they were nearly as good as for lighter skin. So what this actually showed is that the developers failed to utilise datasets containing enough darker faces, or test their product with darker faces.\n",
+ "\n",
+ "One of the MIT researchers, Joy Buolamwini, warned, \"We have entered the age of automation overconfident yet underprepared. If we fail to make ethical and inclusive artificial intelligence, we risk losing gains made in civil rights and gender equity under the guise of machine neutrality\".\n",
+ "\n",
+ "Part of the issue appears to be a systematic imbalance in the make up of popular datasets used for training models. The abstract to the paper [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://arxiv.org/abs/1711.08536) states, \"We analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit an observable amerocentric and eurocentric representation bias. Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales\". Here is one of the charts from the paper, showing the geographic make up of what was, at the time (and still, as this book is being written), the two most important image datasets for training models:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The vast majority of the images are from the United States and other Western countries, leading to models trained on ImageNet performing worse on scenes from other countries and cultures. For instance, [research](https://arxiv.org/pdf/1906.02659.pdf) found that such models are worse at identifying household items (such as soap, spices, sofas, or beds) from lower-income countries. Below is an image from the paper, [Does Object Recognition Work for Everyone?](https://arxiv.org/pdf/1906.02659.pdf)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we will discuss shortly, in addition, the vast majority of AI researchers and developers are young white men. Most projects that we have seen do most user testing using friends and families of the immediate product development group. Given this, the kinds of problems we saw above should not be surprising.\n",
+ "\n",
+ "Similar historical bias is found in the texts used as data for natural language processing models. This crops up in downstream machine learning tasks in many ways. For instance, until last year Google Translate showed systematic bias in how it translated the Turkish gender-neutral pronoun \"bir\" into English. For instance, when applied to jobs which are often associated with males, it used \"he\", and when applied to jobs which are often associated with females, it used \"she\":"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also see this kind of bias in online advertisements. For instance, a study in 2019 found that even when the person placing the ad does not intentionally discriminate, Facebook will show the ad to very different audiences used on race and gender. Housing ads with the same text, but changing the picture, between a white family and a black family, were shown to racially different audiences."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Measurement bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the paper [Does Machine Learning Automate Moral Hazard and Error](https://scholar.harvard.edu/files/sendhil/files/aer.p20171084.pdf) in *American Economic Review*, the authors look at a model that tries to answer the question: using historical EHR data, what factors are most predictive of stroke? This are the top predictors from the model:\n",
+ "\n",
+ " - Prior Stroke\n",
+ " - Cardiovascular disease\n",
+ " - Accidental injury\n",
+ " - Benign breast lump\n",
+ " - Colonoscopy\n",
+ " - Sinusitis\n",
+ "\n",
+ "However, only the top two have anything to do with a stroke! Based on what we've studied so far, you can probably guess why. We haven’t really measured *stroke*, which occurs when a region of the brain is denied oxygen due to an interruption in the blood supply. What we’ve measured is who: had symptoms, went to a doctor, got the appropriate tests, AND received a diagnosis of stroke. Actually having a stroke is not the only thing correlated with this complete list — it's also correlated with being the kind of person who actually goes to the doctor (which is influenced by who has access to healthcare, can afford their co-pay, doesn't experience racial or gender-based medical discrimination, and more)! If you are likely to go to the doctor for an *accidental injury*, then you are likely to also go the doctor when you are having a stroke.\n",
+ "\n",
+ "This is an example of *measurement bias*. It occurs when our models make mistakes because we are measuring the wrong thing, or measuring it in the wrong way, or incorporating that measurement into our model inappropriately."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Aggregation Bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Aggregation bias* occurs when models do not aggregate data in a way that incorporates all of the appropriate factors, or when a model does not include the necessary interaction terms, nonlinearities, or so forth. This can particularly occur in medical settings. For instance, the way diabetes is treated is often based on simple univariate statistics and studies involving small groups of heterogeneous people. Analysis of results is often done in a way that does not take account of different ethnicities or genders. However it turns out that diabetes patients have [different complications across ethnicities](https://www.ncbi.nlm.nih.gov/pubmed/24037313), and HbA1c levels (widely used to diagnose and monitor diabetes) [differ in complex ways across ethnicities and genders](https://www.ncbi.nlm.nih.gov/pubmed/22238408). This can result in people being misdiagnosed or incorrectly treated because medical decisions are based on a model which does not include these important variables and interactions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Representation Bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The abstract of the paper [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://arxiv.org/abs/1901.09451) notes that there is gender imbalance in occupations (e.g. females are more likely to be nurses, and males are more likely to be pastors), and says that: \"differences in true positive rates between genders are correlated with existing gender imbalances in occupations, which may compound these imbalances\".\n",
+ "\n",
+ "What this is saying is that the researchers noticed that models predicting occupation did not only reflect the actual gender imbalance in the underlying population, but actually amplified it! This is quite common, particularly for simple models. When there is some clear, easy to see underlying relationship, a simple model will often simply assume that that relationship holds all the time. As the show with the paper, for occupations which had a higher percentage of females, the model tended to overestimate the prevalence of that occupation:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For example, in the training dataset, 14.6% of surgeons were women, yet in the model predictions, only 11.6% of the true positives were women."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Addressing different types of bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Different types of bias require different approaches for mitigation. While gathering a more diverse dataset can address representation bias, this would not help with historical bias or measurement bias. All datasets contain bias. There is no such thing as a completely de-biased dataset. Many researchers in the field have been converging on a set of proposals towards better documenting the decisions, context, and specifics about how and why a particular dataset was created, what scenarios it is appropriate to use in, and what the limitations are. This way, those using the dataset will not be caught off-guard by its biases and limitations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Humans are biased, so does algorithmic bias matter?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We often hear this question — \"humans are biased, so does algorithmic bias even matter?\" This comes up so often, there must be some reasoning that makes sense to the people that ask it, but it doesn't seem very logically sound to us! Independently of whether this is logically sound, it's important to realise that algorithms and people are different. Machine learning, particularly so. Consider these points about machine learning algorithms:\n",
+ "\n",
+ " - *Machine learning can create feedback loops*: small amounts of bias can very rapidly, exponentially increase due to feedback loops\n",
+ " - *Machine learning can amplify bias*: human bias can lead to larger amounts of machine learning bias\n",
+ " - *Algorithms & humans are used differently*: human decision makers and algorithmic decision makers are not used in a plug-and-play interchangeable way in practice. For instance, algorithmic decisions are more likely to be implemented at scale and without a process for recourse. Furthermore, people are more likely to mistakenly believe that the result of an algorithm is objective and error-free.\n",
+ " - *Technology is power*. And with that comes responsibility.\n",
+ "\n",
+ "As the Arkansas healthcare example showed, machine learning is often implemented in practice not because it leads to better outcomes, but because it is cheaper and more efficient. Cathy O'Neill, in her book *Weapons of Math Destruction*, described the pattern of how the privileged are processed by people, the poor are processed by algorithms. This is just one of a number of ways that algorithms are used differently than human decision makers. Others include:\n",
+ "\n",
+ " - People are more likely to assume algorithms are objective or error-free (even if they’re given the option of a human override)\n",
+ " - Algorithms are more likely to be implemented with no appeals process in place\n",
+ " - Algorithms are often used at scale\n",
+ " - Algorithmic systems are cheap."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data contains errors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Because data is likely to contain errors, mechanisms for audits and error-correction are important. A database of suspected gang members maintained by California law enforcement officials was found to be full of errors, including 42 babies who had been added to the database when they were less than 1 year old (28 of whom were marked as *admitting to being gang members*). In this case, there was no process in place for correcting mistakes or removing people once they’ve been added. Another example is the US credit report system; in a large-scale study of credit reports by the FTC in 2012, it was found that 26% of consumers had at least one mistake in their files, and 5% had errors that could be devastating. Yet, the process of getting such errors corrected is incredibly slow and opaque. When public-radio reporter Bobby Allyn discovered that he was erroneously listed as having a firearms conviction, it took him \"more than a dozen phone calls, the handiwork of a county court clerk and six weeks to solve the problem. And that was only after I contacted the company’s communications department as a journalist.\" (as covered in the article [How the careless errors of credit reporting agencies are ruining people’s lives](https://www.washingtonpost.com/posteverything/wp/2016/09/08/how-the-careless-errors-of-credit-reporting-agencies-are-ruining-peoples-lives/))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Disinformation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Disinformation* has a history stretching back hundreds or even thousands of years. It is not necessarily about getting someone to believe something false, but rather, often to sow disharmony and uncertainty, and to get people to give up on seeking the truth. Receiving conflicting accounts can lead people to assume that they can never know what to trust.\n",
+ "\n",
+ "Some people think disinformation is primarily about false information or *fake news*, but in reality, disinformation can often contain seeds of truth, or involve half-truths taken out of context. Ladislav Bittman, who was an intelligence officer in the USSR who later defected to the United States and wrote some books in the 1970s and 1980s on the role of disinformation in Soviet propaganda operations. He said, \"Most campaigns are a carefully designed mixture of facts, half-truths, exaggerations, & deliberate lies.\"\n",
+ "\n",
+ "In the United States this has hit close to home in recent years, with the FBI detailing a massive disinformation campaign linked to Russia in the 2016 US election. Understanding the disinformation that was used in this campaign is very educational. For instance, the FBI found that the Russian disinformation campaign often organized two separate fake *grass roots* protests, one for each side of an issue, and got them to protest at the same time! The Houston Chronicle reported on one of these odd events:\n",
+ "\n",
+ "> : A group that called itself the \"Heart of Texas\" had organized it on social media — a protest, they said, against the \"Islamization\" of Texas. On one side of Travis Street, I found about 10 protesters. On the other side, I found around 50 counterprotesters. But I couldn't find the rally organizers. No \"Heart of Texas.\" I thought that was odd, and mentioned it in the article: What kind of group is a no-show at its own event? Now I know why. Apparently, the rally's organizers were in Saint Petersburg, Russia, at the time. \"Heart of Texas\" is one of the internet troll groups cited in Special Prosecutor Robert Mueller's recent indictment of Russians attempting to tamper with the U.S. presidential election."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Disinformation often involves coordinated campaigns of inauthentic behavior. For instance, fraudulent accounts may try to make it seem like many people hold a particular viewpoint. While most of us like to think of ourselves as independent-minded, in reality we evolved to be influenced by others in our in-group, and in opposition to those in our out-group. Online discussions can influence our viewpoints, or alter the range of what we consider acceptable viewpoints. Humans are social animals, and as social animals we are extremely influenced by the people around us. Increasingly, radicalisation occurs in online environments. So influence is coming from people in the virtual space of online forums and social networks.\n",
+ "\n",
+ "Disinformation through auto-generated text is a particularly significant issue, due to the greatly increased capability provided by deep learning. We discuss this issue in depth when we learn to create language models, in <\n",
+ "\n",
+ "Geoff Hinton has told of how even academic papers showing dramatically better results than anything previously published would be rejected from top journals and conferences, just because they used a neural network. Yann Lecun's work on convolutional neural networks, which we will study in the next section, showed that these models could read hand-written text--something that had never been achieved before. However his breakthrough was ignored by most researchers, even as it was used commercially to read 10% of the checks in the US!\n",
+ "\n",
+ "In addition to these three Turing Award winners, there are many other researchers who have battled to get us to where we are today. For instance, Jurgen Schmidhuber (who many believe should have shared in the Turing Award) pioneered many important ideas, including working on the *LSTM* architecture with his student Sepp Hochreiter (widely used for speech recognition and other text modeling tasks, and used in the IMDb example in <| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "29 | \n", + "150 | \n", + "195 | \n", + "254 | \n", + "255 | \n", + "254 | \n", + "176 | \n", + "193 | \n", + "150 | \n", + "96 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "48 | \n", + "166 | \n", + "224 | \n", + "253 | \n", + "253 | \n", + "234 | \n", + "196 | \n", + "253 | \n", + "253 | \n", + "253 | \n", + "253 | \n", + "233 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 3 | \n", + "0 | \n", + "93 | \n", + "244 | \n", + "249 | \n", + "253 | \n", + "187 | \n", + "46 | \n", + "10 | \n", + "8 | \n", + "4 | \n", + "10 | \n", + "194 | \n", + "253 | \n", + "253 | \n", + "233 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 4 | \n", + "0 | \n", + "107 | \n", + "253 | \n", + "253 | \n", + "230 | \n", + "48 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "192 | \n", + "253 | \n", + "253 | \n", + "156 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 5 | \n", + "0 | \n", + "3 | \n", + "20 | \n", + "20 | \n", + "15 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "43 | \n", + "224 | \n", + "253 | \n", + "245 | \n", + "74 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 6 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "249 | \n", + "253 | \n", + "245 | \n", + "126 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 7 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "14 | \n", + "101 | \n", + "223 | \n", + "253 | \n", + "248 | \n", + "124 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 8 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "11 | \n", + "166 | \n", + "239 | \n", + "253 | \n", + "253 | \n", + "253 | \n", + "187 | \n", + "30 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 9 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "16 | \n", + "248 | \n", + "250 | \n", + "253 | \n", + "253 | \n", + "253 | \n", + "253 | \n", + "232 | \n", + "213 | \n", + "111 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "
| 10 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "43 | \n", + "98 | \n", + "98 | \n", + "208 | \n", + "253 | \n", + "253 | \n", + "253 | \n", + "253 | \n", + "187 | \n", + "22 | \n", + "0 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see that the background white pixels are stored as the number zero, black is the number 255, and shades of grey are between the two. This image contains 28 pixels across and 28 pixels down, for a total of 768 pixels. (This is much smaller than an image that you would get from a phone camera, which has millions of pixels, but is a convenient size for our initial learning and experiments. We will build up to bigger, full-colour images soon.)\n",
+ "\n",
+ "So, now you've seen what an image looks like to a computer, let's recall our goal: create a model that can recognise “3”s and “7”s. How might you go about getting a computer to do that?\n",
+ "\n",
+ "> stop: Before you read on, take a moment to think about how a computer might be able to recognize these two different digits. What kind of features might it be able to look at? How might it be able to identify these features? How could it combine them together? Learning works best when you try to solve problems yourself, rather than just reading somebody else's answers; so step away from this book for a few minutes, grab a piece of paper and pen, and jot some ideas down…"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## First try: pixel similarity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, here is a first idea: how about we find the average pixel value for every pixel of the threes and do the same for each of the sevens. Then, to classify a digit see which of these two group averages it is most similar to. This certainly seems like it should be better than nothing, so it will make a good baseline."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: A _baseline_ is a simple model which you are confident should perform reasonably well. It should be very simple to implement, and very easy to test, so that you can then test each of your improved ideas, and make sure they are always better than your baseline. Without starting with a sensible baseline, it is very difficult to know whether your super fancy models are actually any good. One good approach to creating a baseline is doing what we have done here: think of a simple, easy to implement model. Another good approach is to search around to find other people that have solved similar problems to yours, and download and run their code on your dataset. Ideally, try both of these!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Step one for our simple model is to get the average of pixel values for each of our two groups. In the process of doing this, we will learn a lot of neat Python numeric programming tricks!\n",
+ "\n",
+ "Let's create a tensor containing all of our threes stacked together. We already know how to create a tensor containing a single image. To create a tensor for every image in a directory, we can use a list comprehension. (Notice also that we use Jupyter to do some little checks of our work along the way; in this case, making sure that the number of returned items seems reasonable):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(6131, 6265)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "seven_tensors = [tensor(Image.open(o)) for o in sevens]\n",
+ "three_tensors = [tensor(Image.open(o)) for o in threes]\n",
+ "len(three_tensors),len(seven_tensors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: List and dictionary comprehensions are a wonderful feature of Python. Many Python programmers use them every day, including all of the authors of this book—they are part of \"idiomatic Python\". But programmers coming from other languages may have never seen them before. There are a lot of great tutorials just a web search away, so we won't spend a long time discussing them now. Here is a quick explanation and example to get you started. A list comprehension looks like this: `new_list = [f(o) for o in a_list if o>0]`. This would return every element of `a_list` that is greater than zero, after passing it to the function `f`. There are three parts here: the collection you are iterating over (`a_list`), an optional filter (`if o>0`), and something to do to each element (`f(o)`). It's not only shorter to write but way faster than the alternative ways of creating the same list with a loop."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll also check that one of the images looks okay. Since we now have tensors (which Jupyter by default will print as values), rather than PIL images (which Jupyter by default will display as an image), we need to use fastai's show_image function to display it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_image(three_tensors[1]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We want to take the average of the pixels, which means we need to combine all of the items of this list into a single three-dimensional tensor. The most common way to describe such a tensor is to call it a *rank-3 tensor*. We often need to stack up individual tensors in a collection into a single tensor. Unsurprisingly, PyTorch comes with a function called `stack`. Some things in PyTorch, such as taking a mean, require us to cast our integer types to float types. Since we'll be needing this later, we'll cast our tensor to `float` now. Casting in PyTorch is as simple as typing the name of the type you wish to cast to, and treating it as a method. Generally when images are floats, the pixels are expected to be be zero and one, so we also divide by 255 here."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([6131, 28, 28])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stacked_sevens = torch.stack(seven_tensors).float()/255\n",
+ "stacked_threes = torch.stack(three_tensors).float()/255\n",
+ "stacked_threes.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Perhaps the most important attribute of tensor is its shape. This tells you the length of each axis. In this case, we can see that we have 6131 images, each of size 28 x 28 pixels. There is nothing specifically about this tensor that says that the first axis is the number of images, the second is the height, and the third is the width — the semantics of a tensor are entirely up to us, and how we construct it. As far as PyTorch is concerned, it is just a bunch of numbers in memory.\n",
+ "\n",
+ "The length of a tensor's shape is its rank."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(stacked_threes.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> Important: it's really important for you to commit to memory and practice these bits of tensor jargon: _rank_ is the number of axes or dimensions in a tensor; _shape_ is the size of each axis of a tensor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also get a tensor's rank directly with `ndim`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "stacked_threes.ndim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can calculate the mean across all of these tensors by taking the mean of dimension zero."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEQAAABECAYAAAA4E5OyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAE1klEQVR4nO2byU8jPRTEf2En7AgQO4gDi9hO8P9fOIE4AGIR+xoU1kAgQAKZA6o4eUMU6O7R983IdbE66XYHv3K98rOJ5fN5PByq/usf8H+DHxADPyAGfkAM/IAY1FT4/l9OQbGvPvQMMfADYuAHxMAPiIEfEINKWSYSaL1kW/t9MWKx2JfX5T6PCp4hBpEwxEb+/f0dgFwuB0A2mwXg6emppH1+fgbg9fWVj48PAGprawGIx+MANDc3A9DU1ARAfX19yX3V1dVAeQb9FJ4hBqEYIkZYJmQyGQBub28BuLi4AGBnZweAvb09AM7PzwFIJpOFPsSEvr4+AMbHxwGYnZ0FYHR0FICuri7AMUiMqar6jHFQpniGGARiSDmtkDZcXV0BsL+/D8Da2hoAu7u7ABwcHACOOalUipeXl5J3tLW1AXB6elrS58LCAgBTU1MA9Pf3A44pYkhQeIYYhGKIMoMYoigXZw+AmprP13R2dgJuvo+NjQGf2qNnbm5uSvp4e3sD4O7uDnC6I41Rn8pK+m1eQyJCJFlG0VDkW1tbARgaGgKgo6MDcFEX5CFyuVwhIx0dHQFwcnICOF3SO8RGsbOc+w0KzxCDUAyRoivSmse6lvIrGymqgqKayWRIJBIAXF9fl/Ste6RD8il6V11dXcn93qlGjEAMURQUFUVP19ISO8/FFGWfx8dH4NNjbGxsALC1tQU4DWloaAAc2wYGBgCXXRobGwHHyrDwDDGIREMsY8QMtVrjKMtcXl4CsLm5CcDq6irr6+uAc7fqc35+HoDe3l7AOVNlsqjWMIW/KdTT/yBCaUglVyjNSKVSgIv+0tISACsrKwAsLy8XHKhYJScqBrS0tABOU6JmhuAZYhBKQyxTBF0rm2ilKp3Q6lcMSSQSBWbIX6gyJv2RPxHburu7S+6zlbOgiLTIbBd9tjygHytBnJ6eBmBkZKTQh50KelZlABWZ2tvbATeFoiol+iljEMnizk4Zu9iTiVIZUNcyZvl8vsAmlR+TySRAwdI/PDwAcHh4CMDw8DDgmGItfFCj5hliEKpAZDWjXDnAFnGkGcXPSyskmipEizlKy/peDBJTrFELWijyDDH4EUMsI2xrmaPoKDVqnlsUM0T3SDO03NcC0pYrldpticFrSEQIpCG2uKxWURLEEEVLGcBuFcRisd88ixiirKN32ixiWesXdxEjlIbYTWw7nxUt6YLdqC4uHIsRcqTb29uA8yF6l5yptEV9e6f6hxDKh2i+q/CjzSQ5UGUCRdEu3IR0Os3x8THgmCEfoj61ua1FXU9PD+BKi9apBoVniMGPGGLnp90qSKfTgCsEyV1KY3SfXcmmUqlCiUDPaAtzcHAQcI50cnIScAUkOVT5FJ9lIkYgDZGiKyrSBm0JCPf394DTA2UQfS6NyWazBdboGMTMzAwAc3NzACwuLgIwMTEBOA0pVw8JCs8Qg0AaomhK2VUA1haBXcvY+8/OzgDnQuPxeEEjdERCtZNymmFLh2Gzi+AZYhCrcIzgyy8rHcOUY1V2kQtVK99SfBRTkbetdMkew4xg+8H/e8h3EIghZW8uc4S70tFu+N3jVGojgGfIdxApQ/4yeIZ8B35ADPyAGFRyqtH+d85fAM8QAz8gBn5ADPyAGPgBMfADYvALMumtb+Vr5kIAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mean3 = stacked_threes.mean(0)\n",
+ "show_image(mean3);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "According to this dataset, this is the ideal number three! Let's do the same thing for the sevens, but let's put all the steps together at once to save some time:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mean7 = stacked_sevens.mean(0)\n",
+ "show_image(mean7);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's now pick a \"3\", and measure its *distance* from each of these \"ideal digits\".\n",
+ "\n",
+ "> stop: How would you calculate how similar a particular image is from each of our ideal digits? Remember to step away from this book and jot down some ideas, before you move on! Research shows that recall and understanding improves dramatically when you are *engaged* with the learning process by solving problems, experimenting, and trying new ideas yourself\n",
+ "\n",
+ "Here's our sample \"3\":"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEQAAABECAYAAAA4E5OyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAADjElEQVR4nO2aPyh9YRjHP/f4k38L5X+ysohsUpTBhEVMJGUyGAwWg0kGkcFqlMFIyv+kSGIwKWUiUvKn5P/9DXrvcR+He+695957+vV8llPnvvd9n77n2/s8z3tOIBgMothYqQ7Ab6ggAhVEoIIIVBBBeoTf/+cUFHC6qQ4RqCACFUSggghUEIEKIlBBBCqIQAURRKpUPeHh4QGAyclJAI6PjwFYXl4GIBgMEgh8FY59fX0A3N7eAlBTUwNAU1MTAC0tLQmNVR0iCEQ4MYupl7m4uABgYmICgJWVFQDOz8/DxhUVFQFQX18fGvMbxcXFAFxeXsYSkhPay7jBkz1ke3sbgLa2NgBeX18BeH9/B6CzsxOAnZ0dAAoLCwFC+4ZlWXx8fISNXVpa8iK0qFGHCDxxyN3dHQBPT09h98vLywGYmpoCoKys7Nc5LMsKu0p6enrijtMN6hCBJ1nm8/MTgOfn57D75mlnZWVFnOPq6gqAxsZGwM5I2dnZAOzu7gJQW1vrJiQ3aJZxgyd7iHFCTk5OzHNUVlYCdmYyzjDVrYfO+BN1iCApvYzk5eUFgM3NTQCGhoZCzsjMzARgenoagIGBgaTGpg4RJMUhpnIdHh4GYH5+HrDrl++0t7cD0NXVlYzQfqAOESSk25WY+iQ/Px8g1LeYqxMlJSUAlJaWAjAyMgLYvY7pg+LAcYKkCCIxRdjJyUno3tjYGAD7+/t//tcIMjc3B0Bubm6sYWhh5oaUOMSJt7c3wHaPScn9/f2O4w8PDwGoq6uLdUl1iBtSUpg5kZGRAUBFRQUAvb29AKyurgKwsLAQNn5tbQ2IyyGOqEMEvnGIxKTV39JrdXV1QtZVhwh8k2Uke3t7ADQ3NwP2sYDh5uYGgIKCgliX0CzjBt/tIWdnZwAMDg4CP51h6pK8vLyErK8OEfhmDzF1RUdHB2AfIhnMEePp6Slg1y1xoHuIG1K6h1xfXwMwOzvL+Pg48PVpxHfMS+6trS3AE2f8iTpE4KlDzBPf2NgA7I9bHh8fATg4OADg6OgIsM807u/vQ3OkpaUB9qvLmZkZIHFZRaIOEXiaZbq7uwFYXFyMOpDW1lYARkdHAWhoaIh6jijRLOMGTx1iPnIxtUQkzEHy+vo6VVVVXwHFf3jsFnWIG3xTqaYAdYgbVBCBCiJQQQQqiCBSL5O0osAvqEMEKohABRGoIAIVRKCCCP4B/PMI7HrW9/wAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "a_3 = stacked_threes[1]\n",
+ "show_image(a_3);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can't just add up the differences between the pixels of this image and the ideal digit. Why not?...\n",
+ "\n",
+ "Because some will be too high, some will be too low, but overall these will balance out. Instead, there's two main ways data scientists measure *distance* in this context:\n",
+ "\n",
+ "- Take the mean of the *absolute value* of differences (_absolute value_ is the function that replaces negative values with positive values). This is called the *mean absolute difference* or *L1 norm*\n",
+ "- Take the mean of the *square* of differences (which makes everything positive) and then take the *square root* (which *undoes* the squaring). This is called the *root mean squared error (RMSE)* or *L2 norm*.\n",
+ "\n",
+ "> important: in this book we generally assume that you have completed high school maths, and remember at least some of it... But everybody forgets some things! It all depends on what you happen to have had reason to practice in the meantime. Perhaps you have forgotten what a _square root_ is, or exactly how they work. No problem! Any time you come across a maths concept that is not explained fully in this book, don't just keep moving on, but instead stop and look it up. Make sure you understand the basic idea of what that maths concept is, how it works, and why we might be using it. One of the best places to refresh your understanding is Khan Academy. For instance, Khan Academy has a great [introduction to square roots](https://www.khanacademy.org/math/algebra/x2f8bb11595b61c86:rational-exponents-radicals/x2f8bb11595b61c86:radicals/v/understanding-square-roots)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's try both of these now:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(0.1114), tensor(0.2021))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dist_3_abs = (a_3 - mean3).abs().mean()\n",
+ "dist_3_sqr = ((a_3 - mean3)**2).mean().sqrt()\n",
+ "dist_3_abs,dist_3_sqr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(0.1586), tensor(0.3021))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dist_7_abs = (a_3 - mean7).abs().mean()\n",
+ "dist_7_sqr = ((a_3 - mean7)**2).mean().sqrt()\n",
+ "dist_7_abs,dist_7_sqr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In both cases, the distance between our `3` and the \"ideal\" `3` is less than the distance to the ideal `7`. So our simple model will give the right prediction in this case."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> s: Intuitively, the difference between L1 norm and mean squared error (*MSE*) is that the latter will penalize more heavily bigger mistakes than the former (and be more lenient with small mistakes)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "PyTorch already provides both of these as *loss functions*. You'll find these inside `torch.nn.functional`, which the PyTorch team recommends importing as `F` (and is available by default under that name in fastai). Here *MSE* stands for *mean squared error*, and *L1* refers to the standard mathematical jargon for *mean absolute value* (in math it's called the *L1 norm*)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(0.1586), tensor(0.3021))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "F.l1_loss(a_3.float(),mean7), F.mse_loss(a_3,mean7).sqrt()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: When I first came across this \"L1\" thingie, I looked it up to see what on Earth it meant, found on Google that it is a _vector norm_ using _absolute value_, so looked up _vector norm_ and started reading: _Given a vector space V over a field F of the real or complex numbers, a norm on V is a nonnegative-valued any function p: V → \\[0,+∞) with the following properties: For all a ∈ F and all u, v ∈ V, p(u + v) ≤ p(u) + p(v)..._ Then I stopped reading. \"Ugh, I'll never understand math!\" I thought, for the thousandth time. Since then I've learned that every time these complex mathy bits of jargon come up in practice, it turns out I can replace them with a tiny bit of code! Like the _L1 loss_ is just equal to `(a-b).abs().mean()`, where `a` and `b` are tensors. I guess mathy folks just think differently to me... I'll make sure, in this book, every time some mathy jargon comes up, I'll give you the little bit of code it's equal to as well, and explain in common sense terms what's going on."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### NumPy arrays and PyTorch tensors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the above code we completed various mathematical operations on *PyTorch tensors*. If you've done some numeric programming in Pytorch before, you may recognize these as being similar to *Numpy arrays*. [Numpy](https://numpy.org/) is the most widely used library for scientific and numeric programming in Python, and provides very similar functionality and a very similar API to that provided by PyTorch; however, it does not support using the GPU, or calculating gradients, which are both critical for deep learning. Therefore, in this book we will generally use PyTorch tensors instead of NumPy arrays, where possible. (Note that fastai adds some features to NumPy and PyTorch to make them a bit more similar to each other; if any code in this book doesn't work on your computer, it's possible that you forgot to include a line at the start of your notebook such as: `from fastai.vision.all import *`.)\n",
+ "\n",
+ "So, what's an array? And what's a tensor?\n",
+ "\n",
+ "And why should you care?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A numpy array is multidimensional table of data, with all items of the same type. Since that can be any type at all, they could even be arrays of arrays, with the innermost array potentially being different sizes — this is called a \"jagged array\". By \"multidimensional table\" we mean, for instance, a list (dimension of one), a table or matrix (dimension of two), a \"table of tables\" or a \"cube\" (dimension of three), and so forth. If the items are all of some simple type such as an integer or a float then numpy will store them as a compact C data structure in memory. This is where numpy shines. Numpy has a wide variety of operators and methods which can run computations on these compact structures at the same speed as optimized C, because they are written in optimized C!\n",
+ "\n",
+ "**Arrays and tensors can finish computations many thousands of times faster than using pure Python!**\n",
+ "A PyTorch tensor is nearly the same thing. It, too, is a multidimensional table of data, with all items of the same type. However, they cannot be just any old type — they have to be a basic numeric type. Therefore, a PyTorch tensor cannot be a jagged array. It is always a regularly shaped multidimensional rectangular structure. The vast majority of methods and operators supported by numpy on these structures are also supported by PyTorch. But PyTorch has the very big benefit that these structures can live on the GPU, in which case this computation will be optimised for the GPU. And furthermore, PyTorch can automatically calculate derivatives of these operations, including combinations of them. As you'll see, it would be impossible to do deep learning in practice without this capability.\n",
+ "\n",
+ "> s: If you don't know what C is, do not worry as you won't need it at all. In a nutshell, it's a low-level (low-level means more similar to the language that computers use internally) language that is very fast compared to Python. To take advantage of its speed while programming in Python, try to avoid as much as possible writing loops and replace them by commands that work directly on arrays or tensors.\n",
+ "\n",
+ "Perhaps the most important new coding skill for a Python programmer to learn is how to effectively use the array/tensor APIs. We will be showing lots more tricks later in this book, but here's a summary of the key things you need to know for now."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To create an array or tensor, pass a list (or list of lists, or list of lists of lists, etc), to `array()` or `tensor()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = [[1,2,3],[4,5,6]]\n",
+ "arr = array (data)\n",
+ "tns = tensor(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 2, 3],\n",
+ " [4, 5, 6]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr # numpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[1, 2, 3],\n",
+ " [4, 5, 6]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tns # pytorch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "All the operations below are shown on tensors - the syntax and results for NumPy arrays is idential.\n",
+ "\n",
+ "You can select a row:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([4, 5, 6])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tns[1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...or a column, using `:` to indicate *all of the first axis* (we sometimes refer to the dimensions of tensors/arrays as *axes*):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([2, 5])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tns[:,1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can combine these, along with Python slice syntax (`[start:end]`, `end` being excluded)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([5, 6])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tns[1,1:3]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use the standard operators:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[2, 3, 4],\n",
+ " [5, 6, 7]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tns+1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tensors have a type:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'torch.LongTensor'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tns.type()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tensors will automatically change from int to float if needed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[1.5000, 3.0000, 4.5000],\n",
+ " [6.0000, 7.5000, 9.0000]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tns*1.5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Broadcasting and metrics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, is our baseline model any good? To quantify this, we will use a metric. A metric is a number which is calculated from the predictions of our model, and the correct labels in our dataset, and tells us something about how good our model is. For instance, we could use either of the functions we saw in the previous section, mean squared error or mean absolute error, and take the average of them over the whole dataset. However, neither of these are numbers that are very understandable to most people; in practice, we normally use *accuracy* as the metric for classification models.\n",
+ "\n",
+ "As we've discussed, we need to use a *validation set* to calculate our metric. That means we need to do is remove some of the data from training entirely, so it is not seen by the model at all. As it turns out, the creators of the MNIST dataset have already done this for us. Do you remember how there was a whole separate directory called \"valid\"? That's what this directory is for!\n",
+ "\n",
+ "So to start with, let's create tensors for our threes and sevens from that directory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([1010, 28, 28]), torch.Size([1028, 28, 28]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "valid_3_tens = torch.stack([tensor(Image.open(o)) for o in (path/'valid'/'3').ls()])\n",
+ "valid_3_tens = valid_3_tens.float()/255\n",
+ "valid_7_tens = torch.stack([tensor(Image.open(o)) for o in (path/'valid'/'7').ls()])\n",
+ "valid_7_tens = valid_7_tens.float()/255\n",
+ "valid_3_tens.shape,valid_7_tens.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need a function that decides if a digit is a 3 or a 7. We need to know which of our \"ideal digits\" its closer to. First, we need a function that calculates the distance from a dataset to an ideal image. It turns out we can do that very simply, in this case calculating the mean absolute error:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.1114)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def mnist_distance(a,b): return (a-b).abs().mean((-1,-2))\n",
+ "mnist_distance(a_3, mean3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Something very interesting happens when we run this function on the whole set of threes in the validation set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([0.1050, 0.1526, 0.1186, ..., 0.1122, 0.1170, 0.1086]),\n",
+ " torch.Size([1010]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "valid_3_dist = mnist_distance(valid_3_tens, mean3)\n",
+ "valid_3_dist, valid_3_dist.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's returned the distance for every single image, as a vector (i.e. rank 1 tensor) of length 1010 (the number of threes in our validation set). How did that happen? Have a look again at our function `mnist_distance`, and you'll see we have there `(a-b)`. The magic trick is that PyTorch, when it sees two tensors of different ranks, will `broadcast` the tensor with the smaller rank to have the same size as the one with the larger rank. Then, when PyTorch sees an operation on two tensors of the same rank, it completes the operation on each corresponding element of the two tensors, and returns the tensor result. For instance:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([2, 3, 4])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor([1,2,3]) + tensor([1,1,1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So in this case, PyTorch treats `mean3`, a rank 2 tensor representing a single image, as if it was 1010 copies of the same image, and then subtracts each of those copies from each \"three\" in our validation set. What shape would you expect this tensor to have? Try to figure it out yourself before you look at the answer below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1010, 28, 28])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(valid_3_tens-mean3).shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are calculating the difference between the \"ideal 3\" and each of 1010 threes in the validation set, for each of `28x28` images, resulting in the shape `1010,28,28`.\n",
+ "\n",
+ "There's a couple of really cool things to know about this operation we just did:\n",
+ "\n",
+ "- PyTorch doesn't *actually* copy `mean3` 1010 times. Instead, it just *pretends* as if it was a tensor of that shape, but doesn't actually allocate any additional memory\n",
+ "- It does the whole calculation in C (or, if you're using a GPU, in CUDA, the equivalent of C on the GPU), tens of thousands of times faster than pure Python (up to millions of times faster on a GPU!)\n",
+ "\n",
+ "This is true of all broadcasting and elementwise operations and functions done in PyTorch. **It's the most important technique for you to know to create efficient PyTorch code.**\n",
+ "\n",
+ "Next in `mnist_distance` we see `abs()`. You might be able to guess now what this does when applied to a tensor... It applies the method to each individual element in the tensor, and returns a tensor of the results (that is, it applies the method \"elementwise\"). So in this case, we'll get back 1010 absolute values.\n",
+ "\n",
+ "Finally, our function calls `mean((-1,-2))`. In Python, `-1` refers to the last element, and `-2` refers to the second last. So in this case, this tells PyTorch that we want to take the mean of the last two axes of the tensor. After taking the mean over the last two axes, we are left with just the first axis, which is why our final size was `(1010)`.\n",
+ "\n",
+ "We'll be learning lots more about broadcasting throughout this book, especially in <>, and will be practising it regularly too.\n",
+ "\n",
+ "We can use this `mnist_distance` to figure out whether an image is a three or not by using the logic: if the distance between the digit in question and the ideal 3 is less than the distance to the ideal 7, then it's a 3. This function will automatically do broadcasting and be applied elementwise, just like all PyTorch functions and operators."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def is_3(x): return mnist_distance(x,mean3) < mnist_distance(x,mean7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's test it on our example case (note also that when we convert the boolean response to a float, we get a `1.0` for true and `0.0` for false):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(True), tensor(1.))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "is_3(a_3), is_3(a_3).float()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And testing it on the full validation set of threes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([True, True, True, ..., True, True, True])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "is_3(valid_3_tens)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can calculate the accuracy for each of threes and sevens, by taking the average of that function for all threes, and it's inverse for all sevens:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(0.9168), tensor(0.9854), tensor(0.9511))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "accuracy_3s = is_3(valid_3_tens).float() .mean()\n",
+ "accuracy_7s = (1 - is_3(valid_7_tens).float()).mean()\n",
+ "\n",
+ "accuracy_3s,accuracy_7s,(accuracy_3s+accuracy_7s)/2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This looks like a pretty good start! We're getting over 90% accuracy on both threes and sevens.\n",
+ "\n",
+ "But let's be honest: threes and sevens are very different looking digits. And we're only classifying two out of the ten possible digits so far. So we're going to need to do better! To do better, perhaps we should try some deep learning."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Stochastic Gradient descent (SGD)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do you remember the way that Arthur Samuel described machine learning, which we quoted in <>:\n",
+ "\n",
+ "> : _Suppose we arrange for some automatic means of testing the effectiveness of any current weight assignment in terms of actual performance and provide a mechanism for altering the weight assignment so as to maximize the performance. We need not go into the details of such a procedure to see that it could be made entirely automatic and to see that a machine so programed would \"learn\" from its experience._\n",
+ "\n",
+ "As we discussed, this is the key to allowing us to have something which can get better and better — to learn. But our pixel similarity approach does not really do this. We do not have any kind of weight assignment, or any way of improving based on testing the effectiveness of a weight assignment. In other words, we can't really improve our pixel similarity approach by modifying a set of parameters. In order to take advantage of the power of deep learning, we will first have to represent our task in the way that Arthur Samuel described it.\n",
+ "\n",
+ "Instead of trying to find the similarity between an image and a \"ideal image\" we could instead look at each individual pixel, and come up with a set of weights for each pixel, such that the highest weights are associated with those pixels most likely to be black for a particular category. For instance, pixels towards the bottom right are not very likely to be activated for a seven, so they should have a low weight for a seven, but are more likely to be activated for an eight, so they should have a high weight for an eight. This can be represented as a function for each possible category, for instance the probability of being the number eight:\n",
+ "\n",
+ "```\n",
+ "def pr_eight(x,w) = (x*w).sum()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we are assuming that X is the image, represented as a vector. In other words, with all of the rows stacked up end to end into a single long line. And we are assuming that the weights are a vector W. If we have this function, then we just need some way to update the weights to make them a little bit better. With such an approach, we can repeat that step a number of times, making the weights better and better, until they are as good as we can make them.\n",
+ "\n",
+ "We want to find the specific values for the vector W which causes our function to be high for those images that are actually an eight, and low for those images which are not. Searching for the best vector W is a way to search for the best function for recognising eights. (Because we are not yet using a deep neural network, we are limited by what our function can actually do — we are going to fix that constraint later in this chapter.) \n",
+ "\n",
+ "To be more specific, here are the steps that we are going to require, to turn this function into a machine learning classifier:\n",
+ "\n",
+ "1. *Initialize* the weights\n",
+ "1. For each image, use these weights to *predict* whether it appears to be a three or a seven\n",
+ "1. Based on these predictions, calculate how good the model is (its *loss*)\n",
+ "1. Calculate the *gradient*, which measures for each weight, how changing that weight would change the loss\n",
+ "1. *Step* all weights based on that calculation\n",
+ "1. Go back to the second step, and *repeat* the process\n",
+ "1. ...until you decide to *stop* the training process (for instance because the model is good enough, or you don't want to wait any longer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#id gradient_descent\n",
+ "#caption The gradient descent process\n",
+ "#alt Graph showing the steps for Gradient Descent\n",
+ "gv('''\n",
+ "init->predict->loss->gradient->step->stop\n",
+ "step->predict[label=repeat]\n",
+ "''')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These seven steps are the key to the training of all deep learning models， and we'll be using the seven terms in the above diagram throughout this book. That deep learning turns out to rely entirely on these steps is extremely surprising and counter-intuitive. It's amazing that this process can solve such complex problems. But, as you'll see, it really does!\n",
+ "\n",
+ "There are many different ways to do each of these seven steps, and we will be learning about them throughout the rest of this book. These are the details which make a big difference for deep learning practitioners. But it turns out that the general approach to each one generally follows some basic principles:\n",
+ "\n",
+ "- **Initialize**: we initialise the weights to random values. This may sound surprising. There are certainly other choices we could make, such as initialising them to the percentage of times that that pixel is activated for that category. But since we already know that we have a routine to improve these weights, it turns out that just starting with random weights works perfectly well\n",
+ "- **Loss**: This is the thing Arthur Samuel refered to: \"*testing the effectiveness of any current weight assignment in terms of actual performance*\". We need some function that will return a number that is small if the performance of the model is good, and vice versa (the standard approach is to treat a small loss as good, and a large loss as bad, although this is just a convention)\n",
+ "- **Step**: A simple way to figure out whether a weight should be increased a bit, or decreased a bit, would be just to try it. Increase the weight by a small amount, and see if the loss goes up or down. Once you find the correct direction, you could then change that amount by a bit more, and a bit less, until you find an amount which works well. However, this is slow! As we will see, the magic of calculus allows us to directly figure out which direction, and roughly how much, to change each weight, without having to try all these small changes, by calculating *gradients*. This is just a performance optimisation, we would get exactly the same results by using the slower manual process as well\n",
+ "- **Stop**: We have already discussed how to choose how many epochs to train a model for. This is where that decision is applied. For our digit classifier, we would keep training until the accuracy of the model started getting worse, or we ran out of time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at a picture of what this would look like. First we will define a very simple function, the quadratic — let's pretend that this is our loss function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def f(x): return x**2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is a graph of that function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_function(f, 'x', 'x**2')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The sequence of steps we described above starts by picking some random value for a parameter, and calculating the value of the loss:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_function(f, 'x', 'x**2')\n",
+ "plt.scatter(-1.5, f(-1.5), color='red');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we look to see what would happen if we increased or decreased our parameter by a little bit — the *adjustment*. This is simply the slope at a particular point:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"A](\"images/grad_illustration.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can change our weight by a little in the direction of the slop, calculate our loss and adjustment again, and repeat this a few times. Eventually, we will get to the lowest point on our curve:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"An](\"images/chapter2_perfect.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This basic idea goes all the way back to Isaac Newton, who pointed out that we can optimise arbitrary functions in this way. Regardless of how complicated our functions become, this basic approach of gradient descent will not significantly change. The only minor changes we will see later in this book are some handy ways we can make it faster, by finding better steps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The gradient"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The one magic step is the bit where we calculate the *gradients*. As we mentioned, we can use calculus as a performance optimization; it allows us to more quickly calculate whether our loss will go up or down when we adjust our parameters up or down. In other words, the gradients will tell us how much we have to change each weight to make our model better.\n",
+ "\n",
+ "Perhaps you remember back to your high school calculus class: the *derivative* of a function tells you how much a change in the parameters of a function will change its result. Don't worry, lots of us forget our calculus once high school is behind us! But you will have to have some intuitive understanding of what a derivative is before you continue, so if this is all very fuzzy in your head, head over to Khan Academy and complete the lessons on basic derivatives. You won't have to know how to calculate them yourselves, you just have to know what a derivative is.\n",
+ "\n",
+ "The key point about a derivative is this: for any function, such as the quadratic function we saw before, we can calculate its derivative. The derivative is another function. It calculates the change, rather than the value. For instance, the derivative of the quadratic function at the value three tells us how rapidly the function changes at the value three. More specifically, you may remember from high school that gradient is defined as \"rise/run\", that is, the change in the value of the function, divided by the change in the value of the parameter. When we know how our function will change, then we know what we need to do to make it smaller. This is the key to machine learning: having a way to change the parameters of a function to make it smaller. Calculus provides us with a computational shortcut, the derivative, which lets us directly calculate the gradient of our functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One important thing to be aware of: our function has lots of weights that we need to adjust, so when we calculate the derivative we won't get back one number, but lots of them — a gradient for every weight. But there is nothing mathematically tricky here; you can calculate the derivative with respect to one weight, and treat all the other ones as constant. Then repeat that for each weight. This is how all of the gradients are calculated, for every weight.\n",
+ "\n",
+ "We mentioned just now that you won't have to calculate any gradients yourselves. How can that be? Amazingly enough, PyTorch is able to automatically compute the derivative of nearly any function! What's more, it does it very fast. Most of the time, it will be at least as fast as any derivative function that you can create by hand. Let's see an example. First, pick a value (which must be a tensor) we want gradients at:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xt = tensor(3.).requires_grad_()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice the special method `requires_grad_`? That's the magical incantation we use to tell PyTorch that we want to calculate gradients for that value.\n",
+ "\n",
+ "Now we calculate our function with that value (notice how PyTorch prints not just the value calculated, but also a note that it has a gradient function it'll be using to calculate our gradient when needed):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(9., grad_fn=)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "yt = f(xt)\n",
+ "yt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we tell PyTorch to calculate the gradients for us:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "yt.backward()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: The \"backward\" here refers to \"back propagation\", which is the name given to the process of calculating the derivative of each layer (we'll see how this is done exactly in chapter , when we calculate the gradients of a deep neural net from scratch). This is called the \"backward pass\" of the network, as opposed to the \"forward pass\", which is where the activations are calculated. Life would probably be easier if `backward` was just called `calculate_grad`, but deep learning folks really do like to add jargon everywhere they can!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now view the gradients by checking the `grad` attribute of our tensor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(6.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xt.grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you remember your high school calculus rules, the derivative of `x**2` is `2*x`, and we have `x=3`, so the gradient should be `2*3=6`, which is what PyTorch calculated for us!\n",
+ "\n",
+ "Now we'll repeat the above steps, but with a vector argument for our function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 3., 4., 10.], requires_grad=True)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xt = tensor([3.,4.,10.]).requires_grad_()\n",
+ "xt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and adding `sum()` to our function so it can take a vector (i.e. a *rank-1 tensor*), and return a scalar (i.e. a *rank-0 tensor*):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(125., grad_fn=)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def f(x): return (x**2).sum()\n",
+ "\n",
+ "yt = f(xt)\n",
+ "yt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our gradients are `2*xt`, as we'd expect!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 6., 8., 20.])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "yt.backward()\n",
+ "xt.grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The loss function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we've seen, if we are going to calculate gradients (which we need), then we need some *loss function* that represents how good our model is. The obvious approach would be to use the accuracy for this purpose. In this case, we would calculate our prediction for each image, and then calculate the overall accuracy (remember, at first we simply use random weights), and then calculate the gradients of each weight with respect to that accuracy calculation.\n",
+ "\n",
+ "Unfortunately, we have a significant technical problem here. The gradient of a function is its *slope*, or its steepness, which can be defined as *rise over run* -- that is, how much the value of function goes up or down, divided by how much you changed the input. We can write this in maths: `(y_new-y_old) / (x_new-x_old)`. Specifically, it is defined when x_new is very similar to x_old, meaning that their difference is very small. But accuracy only changes at all when a prediction changes from a 3 to a 7, or vice versa. So the problem is that a small change in weights from from x_old to x_new isn't likely to cause any prediction to change, so `(y_new - y_old)` will be zero. (In other words, the gradient is zero almost everywhere.) As a result, a very small change in the value of a weight will often not actually change the accuracy at all. This means it is not useful to use accuracy as a loss function. When we use accuracy as a loss function, most of the time our gradients will actually be zero, and the model will not be able to learn from that number. That is not much use at all!\n",
+ "\n",
+ "> s: In mathematical terms, accuracy is a function that is constant almost everywhere (except at the threshold, 0.5) so its derivative is nil almost everywhere (and infinity at the threshold). This then gives gradients that are zero or infinite, so useless to do an update of gradient descent.\n",
+ "\n",
+ "Instead, we want a loss function which, when our weights result in slightly better predictions, gives us a slightly better loss. So what does a \"slightly better prediction\" look like, exactly? Well, in this case, it means that, if the correct answer is a 3, then the score is a little higher, or if the correct answer is a 7, then the score is a little lower. Here is a simple implementation of just such a function, assuming that `inputs` are numbers between zero and one:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mnist_loss(inputs, targets):\n",
+ " return torch.where(targets==1, 1-inputs, inputs).mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, we're assuming that `targets` contains `1` for any digit which is meant to be a three, and `0` otherwise. Let's look at an example:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tgt = tensor([1,0,1])\n",
+ "inp = tensor([0.9, 0.4, 0.2])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`torch.where(a,b,c)` is the same as running the list comprehension `[b[i] if a[i] else c[i] for i in range(len(a))]`, except it works on tensors, at C/CUDA speed. (It's important to learn about PyTorch functions like this, because looping over tensors in Python performs at Python speed, not C/CUDA speed!) Try running `help(torch.where)` now to read the docs for this function, or, better still, look it up on the PyTorch documentation site."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0.1000, 0.4000, 0.8000])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.where(tgt==1, 1-inp, inp)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see that this function will return a lower number if the predictions are more accurate, and more confident for accurate predictions (higher absolute values) and less confident for inaccurate predictions. In PyTorch, we always assume that a lower value of a loss function is better."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.4333)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mnist_loss(inp,tgt)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For instance, if we change our prediction for the one \"false\" target from `0.2` to `0.8` the loss will go down, indicating that this is a better prediction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.2333)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mnist_loss(tensor([0.9, 0.4, 0.8]),tgt)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sigmoid"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One problem with `mnist_loss` as currently defined is that it assumes that inputs are always between zero and one. We need to ensure, then, that this is actually the case! As it happens, there is a function that does exactly that--it always outputs a number between one and one. This function is called *sigmoid* and is defined by:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(x): return 1/(1+torch.exp(-x))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pytorch actually already defines this for us, so we don’t really need our own version. This is an important function in deep learning, since we often want to ensure values between zero and one. This is what it looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can change our weight by a little in the direction of the slop, calculate our loss and adjustment again, and repeat this a few times. Eventually, we will get to the lowest point on our curve:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This basic idea goes all the way back to Isaac Newton, who pointed out that we can optimise arbitrary functions in this way. Regardless of how complicated our functions become, this basic approach of gradient descent will not significantly change. The only minor changes we will see later in this book are some handy ways we can make it faster, by finding better steps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The gradient"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The one magic step is the bit where we calculate the *gradients*. As we mentioned, we can use calculus as a performance optimization; it allows us to more quickly calculate whether our loss will go up or down when we adjust our parameters up or down. In other words, the gradients will tell us how much we have to change each weight to make our model better.\n",
+ "\n",
+ "Perhaps you remember back to your high school calculus class: the *derivative* of a function tells you how much a change in the parameters of a function will change its result. Don't worry, lots of us forget our calculus once high school is behind us! But you will have to have some intuitive understanding of what a derivative is before you continue, so if this is all very fuzzy in your head, head over to Khan Academy and complete the lessons on basic derivatives. You won't have to know how to calculate them yourselves, you just have to know what a derivative is.\n",
+ "\n",
+ "The key point about a derivative is this: for any function, such as the quadratic function we saw before, we can calculate its derivative. The derivative is another function. It calculates the change, rather than the value. For instance, the derivative of the quadratic function at the value three tells us how rapidly the function changes at the value three. More specifically, you may remember from high school that gradient is defined as \"rise/run\", that is, the change in the value of the function, divided by the change in the value of the parameter. When we know how our function will change, then we know what we need to do to make it smaller. This is the key to machine learning: having a way to change the parameters of a function to make it smaller. Calculus provides us with a computational shortcut, the derivative, which lets us directly calculate the gradient of our functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One important thing to be aware of: our function has lots of weights that we need to adjust, so when we calculate the derivative we won't get back one number, but lots of them — a gradient for every weight. But there is nothing mathematically tricky here; you can calculate the derivative with respect to one weight, and treat all the other ones as constant. Then repeat that for each weight. This is how all of the gradients are calculated, for every weight.\n",
+ "\n",
+ "We mentioned just now that you won't have to calculate any gradients yourselves. How can that be? Amazingly enough, PyTorch is able to automatically compute the derivative of nearly any function! What's more, it does it very fast. Most of the time, it will be at least as fast as any derivative function that you can create by hand. Let's see an example. First, pick a value (which must be a tensor) we want gradients at:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xt = tensor(3.).requires_grad_()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice the special method `requires_grad_`? That's the magical incantation we use to tell PyTorch that we want to calculate gradients for that value.\n",
+ "\n",
+ "Now we calculate our function with that value (notice how PyTorch prints not just the value calculated, but also a note that it has a gradient function it'll be using to calculate our gradient when needed):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(9., grad_fn="
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_function(torch.sigmoid, title='Sigmoid', min=-4, max=4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's update `mnist_loss` to first apply `sigmoid` to the inputs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mnist_loss(inputs, targets):\n",
+ " inputs = inputs.sigmoid()\n",
+ " return torch.where(targets==1, 1-inputs, inputs).mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: loss versus metric"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have two terms which are somewhat similar: loss and metric. They are similar because they are both measures of how well your model is performing. The key difference, though, is that the loss must be a function which has a meaningful derivative. It can't have big flat sections, and large jumps, but instead must be reasonably smooth. Therefore, sometimes it does not really reflect exactly what we are trying to achieve, but is something that is a compromise between our real goal, and a function that can be optimised using its gradient. The loss function is calculated for each item in our dataset, and then at the end of an epoch these are all averaged, and the overall mean loss is reported for the epoch.\n",
+ "\n",
+ "Metrics, on the other hand, are the numbers that we really care about. These are the things which are printed at the end of each epoch, and tell us how our model is really doing. It is important that we learn to focus on these metrics, rather than the loss, when judging the performance of a model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Stepping with a learning rate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The gradient only tells us the slope of our function, it doesn't actually tell us how far to adjust the parameters. It gives us some idea of how far to adjust them; if the slope is very large, then that may suggest that we have more adjustments to do, whereas if the slope is very small, that may suggest that we are close to the optimal value.\n",
+ "\n",
+ "Deciding how to change our parameters based on the value of the gradients is an important part of the deep learning process. Nearly all approaches start with the basic idea of multiplying the gradient by some small number, called the *learning rate* (LR). The learning rate is often a number between 0.001 and 0.1, although it could be anything. Often, people select a learning rate just by trying a few, and finding which results in the best model after training (we'll show you a better approach later in this book, called the *learning rate finder*). Once you've picked a learning rate, you can adjust your parameters using this simple function:\n",
+ "\n",
+ "```\n",
+ "w -= gradient(w) * lr\n",
+ "```\n",
+ "\n",
+ "This is known as *stepping* your parameters, using a *optimiser step*.\n",
+ "\n",
+ "If you pick a learning rate that's too low, it can mean having to do for a lot of steps:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"An](\"images/chapter2_small.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although picking a learning rate that's too high is even worse--it can actually result in the loss getting *worse*!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"An](\"images/chapter2_div.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If the learning rate is too high, it may also \"bounce\" around, rather than actually diverging; this has the result of taking many steps to train successfully:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"An](\"images/chapter2_bouncy.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Summarizing gradient descent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To summarize, at the beginning, the weights of our model can be random (training *from scratch*) or come from of a pretrained model (*transfer learning*). In the first case, the output we will get from our inputs won't have anything to do with what we want, and even in the second case, it's very likely the pretrained model won't be very good at the speficic task we are targetting. So the model will need to *learn* better weights.\n",
+ "\n",
+ "To do this, we will compare the outputs the model gives us with our targets (we have labelled data, so we know what result the model should give) using a *loss function*, which returns a number that needs to be as low as possible. Our weights need to be improved. To do this, we take a few data items (such as images) that we feed to our model. After going through our model, we compare to the corresponding targets using our loss function. The score we get tells us how wrong our predictions were, and we will change the weights a little bit to make it slightly better.\n",
+ "\n",
+ "To find how to change the weights to make the loss a bit better, we use calculus to calculate the *gradient* (actually, we let PyTorch do it for us!) Let's imagine you are lost in the mountains with your car parked at the lowest point. To find your way, you might wander in a random direction but that probably won't help much. Since you know you your vehicle is at the lowest point, you would be better to go downhill. By always taking a step in the direction of the steepest slope, you should eventually arrive at your destination. We use the gradient to tell us how big a step to take; specifically, we multiply the gradient by a number we choose called the *learning rate* to decide on the step size."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Stochastic gradient descent and mini-batches"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to take an optimiser step we need to calculate the loss over one or more data items. We could calculate it for the whole dataset, and take the average, or we could calculate it for a single data item. But neither of these sounds ideal — calculating it for the whole dataset would take a very long time, but calculating it for a single item would result in a very imprecise and unstable gradient. So instead we take a compromise between the two: we calculate the average loss for a few data items at a time. This is called a *mini-batch*. The number of data items in the mini batch is called the *batch size*. A larger batch size means that you will get a more accurate and stable estimate of your datasets gradient on the loss function, but it will take longer, and you will get less mini-batches per epoch. Choosing a good batch size is one of the decisions you need to make as a deep learning practitioner to train your model quickly and accurately. We will talk about how to make this choice throughout this book.\n",
+ "\n",
+ "Another good reason for using mini-batches rather than calculating the gradient on individual data items is that, in practice, we nearly always do our training on an accelerator such as a GPU. These accelerators only perform well if they have lots of work to do at a time. So it is helpful if we can give them lots of data items to work on at a time. Using mini-batches is one of the best ways to do this. (Although if you give them too much data to work on at once, they run out of memory--making GPUs happy is tricky!)\n",
+ "\n",
+ "As we've seen, in the discussion of data augmentation, we get better generalisation if we can very things during training. A simple and effective thing we can vary during training is what data items we put in each mini batch. Rather than simply enumerating our data set in order for every epoch, instead what we normally do in practice is to randomly shuffle it on every epoch, before we create mini batches. PyTorch and fastai provide a class that will do the shuffling and mini batch collation for you, called `DataLoader`.\n",
+ "\n",
+ "A `DataLoader` can take any Python collection, and turn it into an iterator over many batches, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[tensor([9, 3, 6, 8, 0]),\n",
+ " tensor([13, 1, 14, 4, 12]),\n",
+ " tensor([ 7, 11, 2, 5, 10])]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "coll = range(15)\n",
+ "dl = DataLoader(coll, batch_size=5, shuffle=True)\n",
+ "list(dl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For training a model, we don't just want any Python collection, but a collection containing independent and dependent variables. A collection that contains tuples of independent and dependent variables is known in PyTorch as a Dataset. Here's an example of an extremely simple Dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#26) [(0, 'a'),(1, 'b'),(2, 'c'),(3, 'd'),(4, 'e'),(5, 'f'),(6, 'g'),(7, 'h'),(8, 'i'),(9, 'j')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds = L(enumerate(string.ascii_lowercase))\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we pass a Dataset to a DataLoader we will get back many batches which are themselves tuples of independent and dependent variable many batches:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(tensor([ 7, 19, 17, 13, 25, 15]), ('h', 't', 'r', 'n', 'z', 'p')),\n",
+ " (tensor([11, 9, 23, 21, 3, 16]), ('l', 'j', 'x', 'v', 'd', 'q')),\n",
+ " (tensor([12, 2, 18, 22, 14, 24]), ('m', 'c', 's', 'w', 'o', 'y')),\n",
+ " (tensor([ 1, 0, 20, 4, 6, 10]), ('b', 'a', 'u', 'e', 'g', 'k')),\n",
+ " (tensor([8, 5]), ('i', 'f'))]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dl = DataLoader(ds, batch_size=6, shuffle=True)\n",
+ "list(dl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Putting it all together"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In code, our process will be implemented something like this for each epoch:\n",
+ "\n",
+ "```python\n",
+ "for x,y in dl:\n",
+ " pred = model(x)\n",
+ " loss = loss_func(pred, y)\n",
+ " loss.backward()\n",
+ " parameters -= parameters.grad * lr\n",
+ "```\n",
+ "\n",
+ "We already have our `x`s--that's the images themselves. We'll concatenate them all into a single tensor, and also change them from a list of matrices (a rank 3 tensor) to a list of vectors (a rank 2 tensor). We can do this using `view`, which is a PyTorch method that changes the shape of a tensor without changing its contents. `-1` is a special parameter to `view`. It means: make this axis as big as necessary to fit all the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_x = torch.cat([stacked_threes, stacked_sevens]).view(-1, 28*28)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need a label for each. We'll use `1` for threes and `0` for sevens:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([12396, 784]), torch.Size([12396, 1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_y = tensor([1]*len(threes) + [0]*len(sevens)).unsqueeze(1)\n",
+ "train_x.shape,train_y.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A Dataset in PyTorch is required to return a tuple of `(x,y)` when indexed. Python provides a `zip` function which, when combined with `list`, provides a simple way to get this functionality:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([784]), tensor([1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dset = list(zip(train_x,train_y))\n",
+ "x,y = dset[0]\n",
+ "x.shape,y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is enough to allow us to create a `DataLoader`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([256, 784]), torch.Size([256, 1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dl = DataLoader(dset, batch_size=256)\n",
+ "xb,yb = first(dl)\n",
+ "xb.shape,yb.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll do the same for the validation set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "valid_x = torch.cat([valid_3_tens, valid_7_tens]).view(-1, 28*28)\n",
+ "valid_y = tensor([1]*len(valid_3_tens) + [0]*len(valid_7_tens)).unsqueeze(1)\n",
+ "valid_dset = list(zip(valid_x,valid_y))\n",
+ "valid_dl = DataLoader(valid_dset, batch_size=256)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need an (initially random) weight for every pixel (this is the *initialize* step in our 7-step process):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def init_params(size, std=1.0): return (torch.randn(size)*std).requires_grad_()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "weights = init_params((28*28,1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function `weights*pixels` won't be flexible enough--it is always equal to zero when the pixels are equal to zero (i.e. it's *intercept* is zero). You might remember from high school math that the formula for a line is `y=w*x+b`; we still need the `b`. We'll initialize it to a random number too:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bias = init_params(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In neural networks, the `w` in the equation `y=w*x+b` is called the *weights*, and the `b` is called the *bias*. Together, the weights and bias make up the *parameters*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Parameters: the *weights* and *biases* of a model. The weights are the `w` in the equation `w*x+b`, and the biases are the `b` in that equation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now calculate a prediction for one image:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([4.5118], grad_fn=)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(train_x[0]*weights.T).sum() + bias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need a way to do this for all the images in a mini-batch. Let's create a mini-batch of size 4 for testing:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([4, 784])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch = train_x[:4]\n",
+ "batch.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Whilst we could use a python for loop to calculate the prediction for each image, that would be very slow. Because Python loops don't run on the GPU, and because Python is a slow language for loops in general, we need to represent as much of the computation in a model as possible using higher-level functions.\n",
+ "\n",
+ "In this case, there's an extremely convenient mathematical operation that calculates `w*x` for every row of a matrix--it's called *matrix multiplication*. Here's what matrix multiplication looks like (diagram from Wikipedia):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Matrix](\"images/matmul2.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This image shows two matrices, `A` and `B` being multiplied together. Each item of the result, which we'll call `AB`, contains each item of its corresponding row of `A` multiplied by each item of its corresponding column of `B`, added together. For instance, row 1 column 2 (the orange dot with a red border) is calculated as $a_{1,1} * b_{1,2} + a_{1,2} * b_{2,2}$. If you need a refresher on matrix multiplication, we suggest you take a look at the great *Introduction to Matrix Multiplcation* on *Khan Academy*, since this is the most important mathematical operation in deep learning.\n",
+ "\n",
+ "In Python, matrix multiplication is represented with the `@` operator. Let's try it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[ 4.5118],\n",
+ " [ 3.6536],\n",
+ " [11.2975],\n",
+ " [14.1164]], grad_fn=)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def linear1(xb): return xb@weights + bias\n",
+ "preds = linear1(batch)\n",
+ "preds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first element is the same as we calculated before, as we'd expect. This equation, `batch@weights + bias`, is one of the two fundamental equations of any neural network (the other one is the *activation function*, which we'll see in a moment).\n",
+ "\n",
+ "The `mnist_loss` function we wrote earlier already works on a mini-batch, thanks to the magic of broadcasting! Here's the loss for our mini-batch:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.0090, grad_fn=)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "loss = mnist_loss(preds, train_y[:4])\n",
+ "loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can calculate the gradients:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([784, 1]), tensor(-0.0013), tensor([-0.0088]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "loss.backward()\n",
+ "weights.grad.shape,weights.grad.mean(),bias.grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's put that all in a function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def calc_grad(xb, yb, model):\n",
+ " preds = model(xb)\n",
+ " loss = mnist_loss(preds, yb)\n",
+ " loss.backward()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and test it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(-0.0025), tensor([-0.0177]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calc_grad(batch, train_y[:4], linear1)\n",
+ "weights.grad.mean(),bias.grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But look what happens if we call it twice:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(-0.0038), tensor([-0.0265]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calc_grad(batch, train_y[:4], linear1)\n",
+ "weights.grad.mean(),bias.grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The gradients have changed! The reason for this is that `loss.backward` actually *adds* the gradients of `loss` to any gradients that are currently stored. So we have to set the current gradients to zero first."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "weights.grad.zero_()\n",
+ "bias.grad.zero_();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: Methods in PyTorch that end in an underscore modify their object *in-place*. For instance, `bias.zero_()` sets all elements of the tensor `bias` to zero."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our only remaining step will be to update the weights and bias based on the gradient and learning rate. When we do so, we have to tell PyTorch not to take the gradient of this step too, otherwise things will get very confusing! If we assign to the `data` attribute of a tensor then PyTorch will not take the gradient of that step. Here's our basic training loop for an epoch:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train_epoch(model, lr, params):\n",
+ " for xb,yb in dl:\n",
+ " calc_grad(xb, yb, model)\n",
+ " for p in params:\n",
+ " p.data -= p.grad*lr\n",
+ " p.grad.zero_()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also want to know how we're doing, by looking at the accuracy of the validation set. To decide if an output represents a 3 or a 7, we can just check whether it's greater than zero. So our accuracy for each item can be calculated (using broadcasting, so no loops!) with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[True],\n",
+ " [True],\n",
+ " [True],\n",
+ " [True]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(preds>0.0).float() == train_y[:4]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That gives us this function to calculate our validation accuracy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def batch_accuracy(xb, yb):\n",
+ " preds = xb.sigmoid()\n",
+ " correct = (preds>0.5) == yb\n",
+ " return correct.float().mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can check it works:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(1.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch_accuracy(linear1(batch), train_y[:4])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and then putting the batches together:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def validate_epoch(model):\n",
+ " accs = [batch_accuracy(model(xb), yb) for xb,yb in valid_dl]\n",
+ " return round(torch.stack(accs).mean().item(), 4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.4403"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "validate_epoch(linear1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's our starting point. Let's train for one epoch, and see if the accuracy improves:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.4992"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lr = 1.\n",
+ "params = weights,bias\n",
+ "train_epoch(linear1, lr, params)\n",
+ "validate_epoch(linear1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.6772 0.8081 0.914 0.9453 0.9565 0.9619 0.9624 0.9633 0.9658 0.9677 0.9702 0.9716 0.9721 0.9736 0.9741 0.9745 0.9765 0.977 0.977 0.9765 "
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(20):\n",
+ " train_epoch(linear1, lr, params)\n",
+ " print(validate_epoch(linear1), end=' ')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking good! We're already about at the same accuracy as our \"pixel similarity\" approach, and we've created a general purpose foundation we can build on."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating an optimizer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Because this is such a useful general foundation, PyTorch provides some useful classes to make it easier to implement. The first we'll use is to replace our `linear()` function with PyTorch's `nn.Linear` *module*. A \"module\" is an object of a class that inherits from the PyTorch `nn.Module` class. Objects of this class behave identically to a standard Python function, in that you can call it using parentheses, and it will return the activations of a model.\n",
+ "\n",
+ "`nn.Linear` does the same thing as our `init_params` and `linear` together. It contains both the *weights* and *bias* in a single class. Here's how we replicate our model from the previous section:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "linear_model = nn.Linear(28*28,1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Every PyTorch module knows what parameters it has that can be trained; they are available through the `parameters` method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([1, 784]), torch.Size([1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w,b = linear_model.parameters()\n",
+ "w.shape,b.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use this information to create an optimizer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class BasicOptim:\n",
+ " def __init__(self,params,lr): self.params,self.lr = list(params),lr\n",
+ "\n",
+ " def step(self, *args, **kwargs):\n",
+ " for p in self.params: p.data -= p.grad.data * self.lr\n",
+ "\n",
+ " def zero_grad(self, *args, **kwargs):\n",
+ " for p in self.params: p.grad = None"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create our optimizer by passing in the model's parameters:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opt = BasicOptim(linear_model.parameters(), lr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our training loop can now be simplified to:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train_epoch(model):\n",
+ " for xb,yb in dl:\n",
+ " calc_grad(xb, yb, model)\n",
+ " opt.step()\n",
+ " opt.zero_grad()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our validation function doesn't need to change at all:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.6714"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "validate_epoch(linear_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's put our little training loop in a function, to make things simpler:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train_model(model, epochs):\n",
+ " for i in range(epochs):\n",
+ " train_epoch(model)\n",
+ " print(validate_epoch(model), end=' ')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The results are the same as the previous section."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.4932 0.7935 0.8477 0.9165 0.9346 0.9482 0.956 0.9634 0.9658 0.9673 0.9702 0.9717 0.9731 0.9751 0.9756 0.9765 0.9775 0.978 0.9785 0.9785 "
+ ]
+ }
+ ],
+ "source": [
+ "train_model(linear_model, 20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "fastai provides the `SGD` class which, by default, does the same thing as our `BasicOptim`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.4932 0.771 0.8594 0.918 0.9355 0.9492 0.9575 0.9634 0.9658 0.9682 0.9692 0.9717 0.9731 0.9751 0.9756 0.977 0.977 0.9785 0.9785 0.9785 "
+ ]
+ }
+ ],
+ "source": [
+ "linear_model = nn.Linear(28*28,1)\n",
+ "opt = SGD(linear_model.parameters(), lr)\n",
+ "train_model(linear_model, 20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "fastai also provides `Learner.fit`, which we can use instead of `train_model`. To create a `Learner` we first need to create `DataLoaders`, by passing in our training and validation `DataLoader`s:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dls = DataLoaders(dl, valid_dl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To create a `Learner` without using an application (such as `cnn_learner`) we need to pass in all the information that we've created in this chapter: the `DataLoaders`, the model, the optimization function (which will be passed the parameters), the loss function, and optionally any metrics to print:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = Learner(dls, nn.Linear(28*28,1), opt_func=SGD,\n",
+ " loss_func=mnist_loss, metrics=batch_accuracy)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can call `fit`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit(10, lr=lr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, there's nothing magic about the PyTorch and fastai classes. They are just convenient pre-packaged pieces that make your life a bit easier! (They also provide a lot of extra functionality we'll be using in future chapters.)\n",
+ "\n",
+ "With these classes, we can now replace our linear model with a neural network."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Adding a non-linearity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far we have a general procedure for optimising the parameters of a function, and we have tried it out on a very boring function: a simple linear classifier. A linear classifier is very constrained in terms of what it can do. Let's instead use a neural network. Here is the entire definition of a basic neural network:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def simple_net(xb): \n",
+ " res = xb@w1 + b1\n",
+ " res = res.max(tensor(0.0))\n",
+ " res = res@w2 + b2\n",
+ " return res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's it! All we have in `simple_net` is two linear classifiers with a max function between them.\n",
+ "\n",
+ "Here, `w1` and `w2` are weight tensors, and `b1` and `b2` are bias tensors; that is, parameters that are initially randomly initialised, just like we did in the previous section."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "w1 = init_params((28*28,30))\n",
+ "b1 = init_params(30)\n",
+ "w2 = init_params((30,1))\n",
+ "b2 = init_params(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The key point about this is that `w1` has 30 output activations (which means that `w2` must have 30 input activations, so they match). That means that the first layer can construct 30 different features, each representing some different mix of pixels. You can change that `30` to anything you like, to make the model more or less complex.\n",
+ "\n",
+ "That little function `res.max(tensor(0.0))` is called a *rectified linear unit*, also known as *ReLU*. I think we can all agree that *rectified linear unit* sounds pretty fancy and complicated... But actually, there's nothing more to it than `res.max(tensor(0.0))`, in other words: replace every negative number with a zero. This tiny function is also available in PyTorch as `F.relu`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although picking a learning rate that's too high is even worse--it can actually result in the loss getting *worse*!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If the learning rate is too high, it may also \"bounce\" around, rather than actually diverging; this has the result of taking many steps to train successfully:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Summarizing gradient descent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To summarize, at the beginning, the weights of our model can be random (training *from scratch*) or come from of a pretrained model (*transfer learning*). In the first case, the output we will get from our inputs won't have anything to do with what we want, and even in the second case, it's very likely the pretrained model won't be very good at the speficic task we are targetting. So the model will need to *learn* better weights.\n",
+ "\n",
+ "To do this, we will compare the outputs the model gives us with our targets (we have labelled data, so we know what result the model should give) using a *loss function*, which returns a number that needs to be as low as possible. Our weights need to be improved. To do this, we take a few data items (such as images) that we feed to our model. After going through our model, we compare to the corresponding targets using our loss function. The score we get tells us how wrong our predictions were, and we will change the weights a little bit to make it slightly better.\n",
+ "\n",
+ "To find how to change the weights to make the loss a bit better, we use calculus to calculate the *gradient* (actually, we let PyTorch do it for us!) Let's imagine you are lost in the mountains with your car parked at the lowest point. To find your way, you might wander in a random direction but that probably won't help much. Since you know you your vehicle is at the lowest point, you would be better to go downhill. By always taking a step in the direction of the steepest slope, you should eventually arrive at your destination. We use the gradient to tell us how big a step to take; specifically, we multiply the gradient by a number we choose called the *learning rate* to decide on the step size."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Stochastic gradient descent and mini-batches"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to take an optimiser step we need to calculate the loss over one or more data items. We could calculate it for the whole dataset, and take the average, or we could calculate it for a single data item. But neither of these sounds ideal — calculating it for the whole dataset would take a very long time, but calculating it for a single item would result in a very imprecise and unstable gradient. So instead we take a compromise between the two: we calculate the average loss for a few data items at a time. This is called a *mini-batch*. The number of data items in the mini batch is called the *batch size*. A larger batch size means that you will get a more accurate and stable estimate of your datasets gradient on the loss function, but it will take longer, and you will get less mini-batches per epoch. Choosing a good batch size is one of the decisions you need to make as a deep learning practitioner to train your model quickly and accurately. We will talk about how to make this choice throughout this book.\n",
+ "\n",
+ "Another good reason for using mini-batches rather than calculating the gradient on individual data items is that, in practice, we nearly always do our training on an accelerator such as a GPU. These accelerators only perform well if they have lots of work to do at a time. So it is helpful if we can give them lots of data items to work on at a time. Using mini-batches is one of the best ways to do this. (Although if you give them too much data to work on at once, they run out of memory--making GPUs happy is tricky!)\n",
+ "\n",
+ "As we've seen, in the discussion of data augmentation, we get better generalisation if we can very things during training. A simple and effective thing we can vary during training is what data items we put in each mini batch. Rather than simply enumerating our data set in order for every epoch, instead what we normally do in practice is to randomly shuffle it on every epoch, before we create mini batches. PyTorch and fastai provide a class that will do the shuffling and mini batch collation for you, called `DataLoader`.\n",
+ "\n",
+ "A `DataLoader` can take any Python collection, and turn it into an iterator over many batches, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[tensor([9, 3, 6, 8, 0]),\n",
+ " tensor([13, 1, 14, 4, 12]),\n",
+ " tensor([ 7, 11, 2, 5, 10])]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "coll = range(15)\n",
+ "dl = DataLoader(coll, batch_size=5, shuffle=True)\n",
+ "list(dl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For training a model, we don't just want any Python collection, but a collection containing independent and dependent variables. A collection that contains tuples of independent and dependent variables is known in PyTorch as a Dataset. Here's an example of an extremely simple Dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#26) [(0, 'a'),(1, 'b'),(2, 'c'),(3, 'd'),(4, 'e'),(5, 'f'),(6, 'g'),(7, 'h'),(8, 'i'),(9, 'j')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds = L(enumerate(string.ascii_lowercase))\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we pass a Dataset to a DataLoader we will get back many batches which are themselves tuples of independent and dependent variable many batches:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(tensor([ 7, 19, 17, 13, 25, 15]), ('h', 't', 'r', 'n', 'z', 'p')),\n",
+ " (tensor([11, 9, 23, 21, 3, 16]), ('l', 'j', 'x', 'v', 'd', 'q')),\n",
+ " (tensor([12, 2, 18, 22, 14, 24]), ('m', 'c', 's', 'w', 'o', 'y')),\n",
+ " (tensor([ 1, 0, 20, 4, 6, 10]), ('b', 'a', 'u', 'e', 'g', 'k')),\n",
+ " (tensor([8, 5]), ('i', 'f'))]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dl = DataLoader(ds, batch_size=6, shuffle=True)\n",
+ "list(dl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Putting it all together"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In code, our process will be implemented something like this for each epoch:\n",
+ "\n",
+ "```python\n",
+ "for x,y in dl:\n",
+ " pred = model(x)\n",
+ " loss = loss_func(pred, y)\n",
+ " loss.backward()\n",
+ " parameters -= parameters.grad * lr\n",
+ "```\n",
+ "\n",
+ "We already have our `x`s--that's the images themselves. We'll concatenate them all into a single tensor, and also change them from a list of matrices (a rank 3 tensor) to a list of vectors (a rank 2 tensor). We can do this using `view`, which is a PyTorch method that changes the shape of a tensor without changing its contents. `-1` is a special parameter to `view`. It means: make this axis as big as necessary to fit all the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_x = torch.cat([stacked_threes, stacked_sevens]).view(-1, 28*28)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need a label for each. We'll use `1` for threes and `0` for sevens:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([12396, 784]), torch.Size([12396, 1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_y = tensor([1]*len(threes) + [0]*len(sevens)).unsqueeze(1)\n",
+ "train_x.shape,train_y.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A Dataset in PyTorch is required to return a tuple of `(x,y)` when indexed. Python provides a `zip` function which, when combined with `list`, provides a simple way to get this functionality:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([784]), tensor([1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dset = list(zip(train_x,train_y))\n",
+ "x,y = dset[0]\n",
+ "x.shape,y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is enough to allow us to create a `DataLoader`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([256, 784]), torch.Size([256, 1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dl = DataLoader(dset, batch_size=256)\n",
+ "xb,yb = first(dl)\n",
+ "xb.shape,yb.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll do the same for the validation set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "valid_x = torch.cat([valid_3_tens, valid_7_tens]).view(-1, 28*28)\n",
+ "valid_y = tensor([1]*len(valid_3_tens) + [0]*len(valid_7_tens)).unsqueeze(1)\n",
+ "valid_dset = list(zip(valid_x,valid_y))\n",
+ "valid_dl = DataLoader(valid_dset, batch_size=256)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need an (initially random) weight for every pixel (this is the *initialize* step in our 7-step process):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def init_params(size, std=1.0): return (torch.randn(size)*std).requires_grad_()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "weights = init_params((28*28,1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function `weights*pixels` won't be flexible enough--it is always equal to zero when the pixels are equal to zero (i.e. it's *intercept* is zero). You might remember from high school math that the formula for a line is `y=w*x+b`; we still need the `b`. We'll initialize it to a random number too:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bias = init_params(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In neural networks, the `w` in the equation `y=w*x+b` is called the *weights*, and the `b` is called the *bias*. Together, the weights and bias make up the *parameters*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Parameters: the *weights* and *biases* of a model. The weights are the `w` in the equation `w*x+b`, and the biases are the `b` in that equation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now calculate a prediction for one image:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([4.5118], grad_fn="
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This image shows two matrices, `A` and `B` being multiplied together. Each item of the result, which we'll call `AB`, contains each item of its corresponding row of `A` multiplied by each item of its corresponding column of `B`, added together. For instance, row 1 column 2 (the orange dot with a red border) is calculated as $a_{1,1} * b_{1,2} + a_{1,2} * b_{2,2}$. If you need a refresher on matrix multiplication, we suggest you take a look at the great *Introduction to Matrix Multiplcation* on *Khan Academy*, since this is the most important mathematical operation in deep learning.\n",
+ "\n",
+ "In Python, matrix multiplication is represented with the `@` operator. Let's try it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[ 4.5118],\n",
+ " [ 3.6536],\n",
+ " [11.2975],\n",
+ " [14.1164]], grad_fn=| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "batch_accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.636918 | \n", + "0.503445 | \n", + "0.495584 | \n", + "00:00 | \n", + "
| 1 | \n", + "0.500283 | \n", + "0.192597 | \n", + "0.839549 | \n", + "00:00 | \n", + "
| 2 | \n", + "0.184349 | \n", + "0.182295 | \n", + "0.833660 | \n", + "00:00 | \n", + "
| 3 | \n", + "0.081278 | \n", + "0.107260 | \n", + "0.912169 | \n", + "00:00 | \n", + "
| 4 | \n", + "0.043316 | \n", + "0.078320 | \n", + "0.932777 | \n", + "00:00 | \n", + "
| 5 | \n", + "0.028503 | \n", + "0.062712 | \n", + "0.946025 | \n", + "00:00 | \n", + "
| 6 | \n", + "0.022414 | \n", + "0.052999 | \n", + "0.955348 | \n", + "00:00 | \n", + "
| 7 | \n", + "0.019704 | \n", + "0.046531 | \n", + "0.962218 | \n", + "00:00 | \n", + "
| 8 | \n", + "0.018323 | \n", + "0.041979 | \n", + "0.965653 | \n", + "00:00 | \n", + "
| 9 | \n", + "0.017486 | \n", + "0.038622 | \n", + "0.966634 | \n", + "00:00 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_function(F.relu)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: There is an enormous amount of jargon in deep learning, such as: _rectified linear unit_. The vast vast majority of this jargon is no more complicated than can be implemented in a short line of code and Python, as we saw in this example. The reality is that for academics to get their papers published they need to make them sound as impressive and sophisticated as possible. One of the ways that they do that is to introduce jargon. Unfortunately, this has the result that the field ends up becoming far more intimidating and difficult to get into than it should be. You do have to learn the jargon, because otherwise papers and tutorials are not going to mean much to you. But that doesn't mean you have to find the jargon intimidating. Just remember, when you come across a word or phrase that you haven't seen before, it will almost certainly turn out that it is a very simple concept that it is referring to."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The basic idea is that by using more linear layers, we can have our model do more computation, and therefore model more complex functions. But there's no point just putting one linear layout directly after another one, because when we multiply things together and then at them up multiple times, that can be replaced by multiplying different things together and adding them up just once! That is to say, a series of any number of linear layers in a row can be replaced with a single linear layer with a different set of parameters.\n",
+ "\n",
+ "But if we put a non-linear function between them, such as max, then this is no longer true. Now, each linear layer is actually somewhat decoupled from the other ones, and can do its own useful work. The max function is particularly interesting, because it operates as a simple \"if\" statement. For any arbitrarily wiggly function, we can approximate it as a bunch of lines joined together; to make it more close to the wiggly function, we just have to use shorter lines.\n",
+ "\n",
+ "Amazingly enough, it can be mathematically proven that this little function can solve any computable problem to an arbitrarily high level of accuracy, if you can find the right parameters for `w1` and `w2`, and if you make these matrices big enough. This is known as the *universal approximation theorem* . The three lines of code that we have here are known as *layers*. The first and third are known as *linear layers*, and the second line of code is known variously as a *nonlinearity*, or *activation function*.\n",
+ "\n",
+ "Just like the previous section, we can replace this code with something a bit simpler, by taking advantage of PyTorch:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simple_net = nn.Sequential(\n",
+ " nn.Linear(28*28,30),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(30,1)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`nn.Sequential` creates a module which will call each of the listed layers or functions in turn.\n",
+ "\n",
+ "`F.relu` is a function, not a PyTorch module. `nn.ReLU` is a PyTorch module that does exactly the same thing. Most functions that can appear in a model also have identical forms that are modules. Generally, it's just a case of replacing `F` with `nn`, and changing the capitalization. When using `nn.Sequential` PyTorch requires us to use the module version. Since modules are classes, we have to instantiate them, which is why you see `nn.ReLU()` above. Because `nn.Sequential` is a module, we can get its parameters--which will return a list of all the parameters of all modules it contains.\n",
+ "\n",
+ "Let's try it out! For deeper models, we may need to use a lower learning rate and a few more epochs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = Learner(dls, simple_net, opt_func=SGD,\n",
+ " loss_func=mnist_loss, metrics=batch_accuracy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "learn.fit(40, 0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're not showing the 40 lines of output here to save room; the training process is recorded in `learn.recorder`, with the table of output stored in the `values` attribute, so we can plot the accuracy over training as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "batch_accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.294820 | \n", + "0.416238 | \n", + "0.504907 | \n", + "00:00 | \n", + "
| 1 | \n", + "0.141692 | \n", + "0.216893 | \n", + "0.816487 | \n", + "00:00 | \n", + "
| 2 | \n", + "0.079073 | \n", + "0.110840 | \n", + "0.921001 | \n", + "00:00 | \n", + "
| 3 | \n", + "0.052444 | \n", + "0.075782 | \n", + "0.941119 | \n", + "00:00 | \n", + "
| 4 | \n", + "0.040078 | \n", + "0.059658 | \n", + "0.957802 | \n", + "00:00 | \n", + "
| 5 | \n", + "0.033729 | \n", + "0.050542 | \n", + "0.962709 | \n", + "00:00 | \n", + "
| 6 | \n", + "0.030057 | \n", + "0.044751 | \n", + "0.965653 | \n", + "00:00 | \n", + "
| 7 | \n", + "0.027653 | \n", + "0.040775 | \n", + "0.967615 | \n", + "00:00 | \n", + "
| 8 | \n", + "0.025914 | \n", + "0.037867 | \n", + "0.969087 | \n", + "00:00 | \n", + "
| 9 | \n", + "0.024563 | \n", + "0.035642 | \n", + "0.970069 | \n", + "00:00 | \n", + "
| 10 | \n", + "0.023465 | \n", + "0.033873 | \n", + "0.972031 | \n", + "00:00 | \n", + "
| 11 | \n", + "0.022547 | \n", + "0.032421 | \n", + "0.972031 | \n", + "00:00 | \n", + "
| 12 | \n", + "0.021761 | \n", + "0.031202 | \n", + "0.973013 | \n", + "00:00 | \n", + "
| 13 | \n", + "0.021081 | \n", + "0.030153 | \n", + "0.974485 | \n", + "00:00 | \n", + "
| 14 | \n", + "0.020482 | \n", + "0.029238 | \n", + "0.974485 | \n", + "00:00 | \n", + "
| 15 | \n", + "0.019949 | \n", + "0.028429 | \n", + "0.975957 | \n", + "00:00 | \n", + "
| 16 | \n", + "0.019472 | \n", + "0.027706 | \n", + "0.976938 | \n", + "00:00 | \n", + "
| 17 | \n", + "0.019039 | \n", + "0.027055 | \n", + "0.977429 | \n", + "00:00 | \n", + "
| 18 | \n", + "0.018645 | \n", + "0.026466 | \n", + "0.977920 | \n", + "00:00 | \n", + "
| 19 | \n", + "0.018283 | \n", + "0.025931 | \n", + "0.977920 | \n", + "00:00 | \n", + "
| 20 | \n", + "0.017950 | \n", + "0.025441 | \n", + "0.978901 | \n", + "00:00 | \n", + "
| 21 | \n", + "0.017641 | \n", + "0.024991 | \n", + "0.979882 | \n", + "00:00 | \n", + "
| 22 | \n", + "0.017353 | \n", + "0.024576 | \n", + "0.979882 | \n", + "00:00 | \n", + "
| 23 | \n", + "0.017084 | \n", + "0.024192 | \n", + "0.980373 | \n", + "00:00 | \n", + "
| 24 | \n", + "0.016832 | \n", + "0.023837 | \n", + "0.980864 | \n", + "00:00 | \n", + "
| 25 | \n", + "0.016595 | \n", + "0.023506 | \n", + "0.981354 | \n", + "00:00 | \n", + "
| 26 | \n", + "0.016371 | \n", + "0.023198 | \n", + "0.981354 | \n", + "00:00 | \n", + "
| 27 | \n", + "0.016159 | \n", + "0.022910 | \n", + "0.981845 | \n", + "00:00 | \n", + "
| 28 | \n", + "0.015959 | \n", + "0.022641 | \n", + "0.981845 | \n", + "00:00 | \n", + "
| 29 | \n", + "0.015768 | \n", + "0.022389 | \n", + "0.981845 | \n", + "00:00 | \n", + "
| 30 | \n", + "0.015587 | \n", + "0.022154 | \n", + "0.981845 | \n", + "00:00 | \n", + "
| 31 | \n", + "0.015414 | \n", + "0.021932 | \n", + "0.981845 | \n", + "00:00 | \n", + "
| 32 | \n", + "0.015249 | \n", + "0.021725 | \n", + "0.981845 | \n", + "00:00 | \n", + "
| 33 | \n", + "0.015092 | \n", + "0.021529 | \n", + "0.982336 | \n", + "00:00 | \n", + "
| 34 | \n", + "0.014941 | \n", + "0.021345 | \n", + "0.982336 | \n", + "00:00 | \n", + "
| 35 | \n", + "0.014796 | \n", + "0.021171 | \n", + "0.982826 | \n", + "00:00 | \n", + "
| 36 | \n", + "0.014658 | \n", + "0.021007 | \n", + "0.982826 | \n", + "00:00 | \n", + "
| 37 | \n", + "0.014524 | \n", + "0.020852 | \n", + "0.982826 | \n", + "00:00 | \n", + "
| 38 | \n", + "0.014396 | \n", + "0.020704 | \n", + "0.983317 | \n", + "00:00 | \n", + "
| 39 | \n", + "0.014272 | \n", + "0.020564 | \n", + "0.983317 | \n", + "00:00 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(L(learn.recorder.values).itemgot(2));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and we can view the final accuracy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.983316957950592"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn.recorder.values[-1][2]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At this point we have something that is rather magical:\n",
+ "\n",
+ "1. A function that can solve any problem to any level of accuracy (the neural network) given the correct set of parameters\n",
+ "1. A way to find the best set of parameters for any function (stochastic gradient descent)\n",
+ "\n",
+ "This is why deep learning can do things which seem rather magical. Believing that this combination of simple techniques can really solve any problem here is one of the biggest steps that we find many students have to take. It seems too good to be true. It seems like things should be more difficult and complicated than this. Our recommendation: try it out! We will take our own recommendation and try this model on the MNIST dataset. Since we are doing everything from scratch ourselves (except for calculating the gradients) you know that there is no special magic hiding behind the scenes…\n",
+ "\n",
+ "There is no need to stop at just two linear layers. We can add as many as we want, as long as we add a nonlinearity between each pair of linear layers. As we will learn, however, the deeper the model gets, the harder it is to optimise the parameters in practice. Later in this book we will learn about some simple but brilliantly effective techniques for training deeper models.\n",
+ "\n",
+ "We already know that a single nonlinearity with two linear layers is enough to approximate any function. So why would we use deeper models? The reason is performance. With a deeper model (that is, one with more layers) we do not need to use as many parameters; it turns out that we can use smaller matrices, with more layers, and get better results than we would get with larger matrices, and few layers.\n",
+ "\n",
+ "That means that we can train them more quickly, and our model will take up less memory. In the 1990s researchers were so focused on the universal approximation theorem that very few were experimenting with more than one nonlinearity. This theoretical but not practical foundation held back the field for years. Some researchers, however, did experiment with deep models, and eventually were able to show that these models could perform much better in practice. Eventually, theoretical results were developed which showed why this happens. Today, it is extremely unusual to find anybody using a neural network with just one nonlinearity.\n",
+ "\n",
+ "Here what happens when we train 18 layer model using the same approach we saw in <>:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls = ImageDataLoaders.from_folder(path)\n",
+ "learn = cnn_learner(dls, resnet18, pretrained=False,\n",
+ " loss_func=F.cross_entropy, metrics=accuracy)\n",
+ "learn.fit_one_cycle(1, 0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Nearly 100% accuracy! That's a big difference compared to our simple neural net. But as you'll learn in the remainder of this book, there are just a few little tricks you need to use to get such great results from scratch yourself. You already know the key foundational pieces. (Of course, even once you know all the tricks, you'll nearly always want to work with the pre-built classes provided by PyTorch and fastai, because they save you having to think about all the little details yourself.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deep learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Congratulations: you now know how to create and train a deep neural network from scratch! There has been quite a few steps to get to this point, but you might be surprised at how simple it really has ended up.\n",
+ "\n",
+ "Now that we are at this point, it is a good opportunity to define, and review, some jargon and concepts.\n",
+ "\n",
+ "The neural network contains a lot of numbers. But those numbers only have one of two types: numbers that are calculated, and the parameters that these are calculated from. This gives us the two most important pieces of jargon to learn:\n",
+ "\n",
+ "- *activations*: numbers that are calculated (both by linear and non-linear layers)\n",
+ "- *parameters*: numbers that are randomly initialised, and optimised (that is, the numbers that define the model)\n",
+ "\n",
+ "We will often talk in this book about activations and parameters. Remember that they have very specific meanings. They are numbers. They are not abstract concepts, but they are actual specific numbers that are in your model. Part of becoming a good deep learning practitioner is getting used to the idea of actually looking at your activations and parameters, and plotting them and testing whether they are behaving correctly.\n",
+ "\n",
+ "Our activations and parameters are all contained in tensors. These are simply regularly shaped arrays. For example, a matrix. Matrices have rows and columns; we call these the *axes* or *dimensions*. The number of dimensions of a tensor is its *rank*. There are some special tensors:\n",
+ "\n",
+ "- rank zero: scalar\n",
+ "- rank one: vector\n",
+ "- rank two: matrix\n",
+ "\n",
+ "A neural network contains a number of layers. Each layer is either linear or nonlinear. We generally alternate between these two kinds of layers in a neural network. Sometimes people refer to both a linear layer and its subsequent nonlinearity together as a single *layer*. Yes, this is confusing. Sometimes a nonlinearity is referred to as an activation function.\n",
+ "\n",
+ "TK: Table jargon recap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### _Choose Your Own Adventure_ reminder"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Did you choose to skip over chapters 2 & 3, in your excitement to peak under the hood? Well, here's your reminder to head back to chapter 2 now, because you'll be needing to know that stuff very soon!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. How is a greyscale image represented on a computer? How about a color image?\n",
+ "1. How are the files and folders in the `MNIST_SAMPLE` dataset structured? Why?\n",
+ "1. Explain how the \"pixel similarity\" approach to classifying digits works.\n",
+ "1. What is a list comprehension? Create one now that selects odd numbers from a list and doubles them.\n",
+ "1. What is a \"rank 3 tensor\"?\n",
+ "1. What is the difference between tensor rank and shape? How do you get the rank from the shape?\n",
+ "1. What are RMSE and L1 norm?\n",
+ "1. How can you apply a calculation on thousands of numbers at once, many thousands of times faster than a Python loop?\n",
+ "1. Create a 3x3 tensor or array containing the numbers from 1 to 9. Double it. Select the bottom right 4 numbers.\n",
+ "1. What is broadcasting?\n",
+ "1. Are metrics generally calculated using the training set, or the validation set? Why?\n",
+ "1. What is SGD?\n",
+ "1. Why does SGD use mini batches?\n",
+ "1. What are the 7 steps in SGD for machine learning?\n",
+ "1. How do we initialize the weights in a model?\n",
+ "1. What is \"loss\"?\n",
+ "1. Why can't we always use a high learning rate?\n",
+ "1. What is a \"gradient\"?\n",
+ "1. Do you need to know how to calculate gradients yourself?\n",
+ "1. Why can't we use accuracy as a loss function?\n",
+ "1. Draw the sigmoid function. What is special about its shape?\n",
+ "1. What is the difference between loss and metric?\n",
+ "1. What is the function to calculate new weights using a learning rate?\n",
+ "1. What does the `DataLoader` class do?\n",
+ "1. Write pseudo-code showing the basic steps taken each epoch for SGD.\n",
+ "1. Create a function which, if passed two arguments `[1,2,3,4]` and `'abcd'`, returns `[(1, 'a'), (2, 'b'), (3, 'c'), (4, 'd')]`. What is special about that output data structure?\n",
+ "1. What does `view` do in PyTorch?\n",
+ "1. What are the \"bias\" parameters in a neural network? Why do we need them?\n",
+ "1. What does the `@` operator do in python?\n",
+ "1. What does the `backward` method do?\n",
+ "1. Why do we have to zero the gradients?\n",
+ "1. What information do we have to pass to `Learner`?\n",
+ "1. Show python or pseudo-code for the basic steps of a training loop.\n",
+ "1. What is \"ReLU\"? Draw a plot of it for values from `-2` to `+2`.\n",
+ "1. What is an \"activation function\"?\n",
+ "1. What's the difference between `F.relu` and `nn.ReLU`?\n",
+ "1. The universal approximation theorem shows that any function can be approximately as closely as needed using just one nonlinearity. So why do we normally use more?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Create your own implementation of `Learner` from scratch, based on the training loop shown in this chapter.\n",
+ "1. Complete all the steps in this chapter using the full MNIST datasets (that is, for all digits, not just threes and sevens). This is a significant project and will take you quite a bit of time to complete! You'll need to do some of your own research to figure out how to overcome some obstacles you'll meet on the way."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05_pet_breeds.ipynb b/05_pet_breeds.ipynb
new file mode 100644
index 000000000..d4cf37cd3
--- /dev/null
+++ b/05_pet_breeds.ipynb
@@ -0,0 +1,2473 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_pet_breeds]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Image classification"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we understand what deep learning is, what it's for, and how to create and deploy a model, it's time for us to go deeper! In an ideal world deep learning practitioners wouldn't have to know every detail of how things work under the hood… But as yet, we don't live in an ideal world. The truth is, to make your model really work, and work reliably, there's a lot of details you have to get right. And a lot of details that you have to check. This process requires being able to look inside your neural network as it trains, and as it makes predictions, find possible problems, and know how to fix them.\n",
+ "\n",
+ "So, from here on in the book we are going to do a deep dive into the mechanics of deep learning. What is the architecture of a computer vision model, an NLP model, a tabular model, and so on. How do you create an architecture which matches the needs of your particular domain? How do you get the best possible results from the training process? How do you make things faster? What do you have to change as your datasets change?\n",
+ "\n",
+ "We will start by repeating the same basic applications that we looked at in the first chapter, but we are going to do two things:\n",
+ "\n",
+ "- make them better;\n",
+ "- apply them to a wider variety of types of data.\n",
+ "\n",
+ "In order to do these two things, we will have to learn all of the pieces of the deep learning puzzle. This includes: different types of layers, regularisation methods, optimisers, putting layers together into architectures, labelling techniques, and much more. We are not just going to dump all of these things out, but we will introduce them only as they are needed to solve an actual problem related to a project we are working on."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From dogs and cats, to pet breeds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In our very first model we learnt how to classify dogs versus cats. Just a few years ago this was considered a very challenging task. But today, it is far too easy! We will not be able to show you the nuances of training models with this problem, because we get the nearly perfect result without worrying about any of the details. But it turns out that the same dataset also allows us to work on a much more challenging problem: figuring out what breed of pet is shown in each image.\n",
+ "\n",
+ "In the first chapter we presented the applications as already solved problems. But this is not how things work in real life. We start with some dataset which we know nothing about. We have to understand how it is put together, how to extract the data we need from it, and what that data looks like. For the rest of this book we will be showing you how to solve these problems in practice, including all of these intermediate steps necessary to understand the data that we working with and test our modelling as we go.\n",
+ "\n",
+ "We have already downloaded the pets dataset. We can get a path to this dataset using the same code we saw in <>:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.vision.all import *\n",
+ "path = untar_data(URLs.PETS)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now if we are going to understand how to extract the breed of each pet from each image were going to need to understand how this data is laid out. Such details of data layout are a vital piece of the deep learning puzzle. Data is usually provided in one of these two ways:\n",
+ "\n",
+ "- Individual files representing items of data, such as text documents or images, possibly organised into folders or with filenames representing information about those items, or\n",
+ "- A table of data, such as in CSV format, where each row is an item, each row which may include filenames providing a connection between the data in the table and data in other formats such as text documents and images.\n",
+ "\n",
+ "There are exceptions to these rules, particularly in domains such as genomics, where there can be binary database formats or even network streams, but overall the vast majority of the datasets your work with use some combination of the above two formats.\n",
+ "\n",
+ "To see what is in our dataset we can use the ls method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#3) [Path('annotations'),Path('images'),Path('models')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that this dataset provides us with \"images\" and \"annotations\" directories. The website for this dataset tells us that the annotations directory contains information about where the pets are rather than what they are. In this chapter we will be doing classification, not localisation, which is to say that we care about what the pets are not where they are. Therefore we will ignore the annotations directory for now. So let's have a look inside the images directory:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#7394) [Path('images/great_pyrenees_173.jpg'),Path('images/wheaten_terrier_46.jpg'),Path('images/Ragdoll_262.jpg'),Path('images/german_shorthaired_3.jpg'),Path('images/american_bulldog_196.jpg'),Path('images/boxer_188.jpg'),Path('images/staffordshire_bull_terrier_173.jpg'),Path('images/basset_hound_71.jpg'),Path('images/staffordshire_bull_terrier_37.jpg'),Path('images/yorkshire_terrier_18.jpg')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(path/\"images\").ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Most functions and methods in fastai which return a collection use a class called `L`. `L` can be thought of as an enhanced version of the ordinary Python `list` type, with added conveniences for common operations. For instance, when we display an object of this class in a notebook it appears in the format you see above. The first thing that is shown is the number of items in the collection, prefixed with a `#`. You'll also see in the above output that the list is suffixed with a \"…\". This means that only the first few items are displayed — which is a good thing, because we would not want more than 7000 filenames on our screen!\n",
+ "\n",
+ "By examining these filenames, we see how they appear to be structured. Each file name contains the pet breed, and then an_character, a number, and finally the file extension. We need to create a piece of code that extracts the breed from a single `Path`. Jupyter notebook makes this easy, because we can gradually build up something that works, and then use it for the entire dataset. We do have to be careful to not make too many assumptions at this point. For instance, if you look carefully you may notice that some of the pet breeds contain multiple words, so we cannot simply break at the first `_` character that we find. To allow us to test our code, let's pick out one of these filenames:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fname = (path/\"images\").ls()[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The most powerful and flexible way to extract information from strings like this is to use a *regular expression*, also known as a *regex*. A regular expression is a special string, written in the regular expression language, which specifies a general rule for for deciding if another string passes a test (i.e., \"matches\" the regular expression), and also possibly for plucking a particular part or parts out of that other string. \n",
+ "\n",
+ "In this case, we need a regular expressin that extracts the pet breed from the file name.\n",
+ "\n",
+ "We do not have the space to give you a complete regular expression tutorial here, particularly because there are so many excellent ones online. And we know that many of you will already be familiar with this wonderful tool. If you're not, that is totally fine — this is a great opportunity for you to rectify that! We find that regular expressions are one of the most useful tools in our programming toolkit, and many of our students tell us that it is one of the things they are most excited to learn about. So head over to Google and search for *regular expressions tutorial* now, and then come back here after you've had a good look around.\n",
+ "\n",
+ "> AG: Not only are regular expresssions dead handy, they also have interesting roots. They are \"regular\" becuase they they were originally examples of a \"regular\" language, the lowest rung within the \"Chomsky hierarchy\", a grammar classification due to the same linguist Noam Chomskey who wrote *Syntactic Structures*, the pioneering work searching for the formal grammar underlying human language. This is one of the charms of computing: it may be that the hammer you reach for every day in fact came from a space ship.\n",
+ "\n",
+ "When you are writing a regular expression, the best way to start is just to try it against one example at first. Let's use the `findall` method to try a regular expression against the filename of the `fname` object:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['great_pyrenees']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "re.findall(r'(.+)_\\d+.jpg$', fname.name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This regular expression plucks out all the characters leading up to the last underscore character, as long as the subsequence characters are numerical digits and then the jpeg file extension.\n",
+ "\n",
+ "Now that we confirmed the regular expression works for the example, let's use it to label the whole dataset. Fastai comes with many classes to help you with your labelling. For labelling with regular expressions, we can use the `RegexLabeller` class. We can use this in the data block API that we saw in <> (in fact, we nearly always use the data block API--it's so much more flexible than the simple factory methods we saw in <>):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pets = DataBlock(blocks = (ImageBlock, CategoryBlock),\n",
+ " get_items=get_image_files, \n",
+ " splitter=RandomSplitter(seed=42),\n",
+ " get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'),\n",
+ " item_tfms=Resize(460),\n",
+ " batch_tfms=aug_transforms(size=224, min_scale=0.75))\n",
+ "dls = pets.dataloaders(path/\"images\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Presizing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One important piece of this `DataBlock` call that we haven't seen before is in these two lines:\n",
+ "\n",
+ "```python\n",
+ "item_tfms=Resize(460),\n",
+ "batch_tfms=aug_transforms(size=224, min_scale=0.75)\n",
+ "```\n",
+ "\n",
+ "These lines implement a fastai data augmentation strategy which we call *presizing*. Presizing is a particular way to do image augmentation, which is designed to minimize data destruction while maintaining good performance.\n",
+ "\n",
+ "We need our images to have the same dimensions, so that they can collate into tensors to be passed to the GPU. We also want to minimize the number of distinct augmentation computations we perform. So the performance requirement suggests that we should, where possible, compose our augmentation transforms into fewer transforms (to reduce the number of computations, and reduce the number of lossy operations) and transform the images into uniform sizes (to run compute efficiently on the GPU).\n",
+ "\n",
+ "The challenge is that, if performed after resizing down to the augmented size, various common data augmentation transforms might introduce spurious empty zones, degrade data, or both. For instance, rotating an image by 45 degrees fills corner regions of the new bounds with emptyness, which will not teach the model anything. Many rotation and zooming operations will require interpolating to create pixels. These interpolated pixels are derived from the original image data but are still of lower quality.\n",
+ "\n",
+ "To workaround these challenges, presizing adopts two strategies:\n",
+ "\n",
+ "1. First, resizing images to relatively \"large dimensions\" that is, dimensions significantly larger than the target training dimensions. \n",
+ "1. Second, composing all of the common augmentation operations (including a resize to the final target size) into one, and performing the combined operation on the GPU only once at the end of processing, rather than performing them individually and interpolating multiple times.\n",
+ "\n",
+ "The first step, the resize, creates images large enough that they have spare margin to allow further augmentation transforms on their inner regions without creating empty zones. This transformation works by resizing to to a square, using a large crop size. On the training set, the crop area is chosen randomly, and the size of the crop is selected to cover the entire width or height of the image, whichever is smaller.\n",
+ "\n",
+ "In the second step, the GPU is used for all data augmentation, and all of the potentially destructive operations are done together, with a single interpolation at the end."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Presizing](\"images/att_00060.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This picture shows the two steps:\n",
+ "\n",
+ "1. *Crop full width or height*: This is in `item_tfms`, so it's applied to each individual image before it is copied to the GPU. It's used to ensure all images are the same size. On the training set, the crop area is chosen randomly. On the validation set, the center square of the image is always chosen\n",
+ "2. *Random crop and augment*: This is in `batch_tfms`, so it's applied to a batch all at once on the GPU, which means it's fast. On the validation set, only the resize to the final size needed for the model is done here. On the training set, the random crop and any other augmentation is done first.\n",
+ "\n",
+ "To implement this process in fastai you use `Resize` as an item transform with a large size, and `RandomResizedCrop` as a batch transform with a smaller size. `RandomResizedCrop` will be added for you if you include the `min_scale` parameter in your `aug_transform` function, as you see in the `DataBlock` call above. Alternatively, you can use `pad` or `squish` instead of `crop` (the default) for the initial `Resize`.\n",
+ "\n",
+ "You can see in this example the difference between an image which has been zoomed, interpolated, rotated, and then interpolated again on the right (which is the approach used by all other deep learning libraries), compared to an image which has been zoomed and rotated as one operation, and then interpolated just once on the left (the fastai approach):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.125685 | \n", + "0.026256 | \n", + "0.992640 | \n", + "00:11 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This picture shows the two steps:\n",
+ "\n",
+ "1. *Crop full width or height*: This is in `item_tfms`, so it's applied to each individual image before it is copied to the GPU. It's used to ensure all images are the same size. On the training set, the crop area is chosen randomly. On the validation set, the center square of the image is always chosen\n",
+ "2. *Random crop and augment*: This is in `batch_tfms`, so it's applied to a batch all at once on the GPU, which means it's fast. On the validation set, only the resize to the final size needed for the model is done here. On the training set, the random crop and any other augmentation is done first.\n",
+ "\n",
+ "To implement this process in fastai you use `Resize` as an item transform with a large size, and `RandomResizedCrop` as a batch transform with a smaller size. `RandomResizedCrop` will be added for you if you include the `min_scale` parameter in your `aug_transform` function, as you see in the `DataBlock` call above. Alternatively, you can use `pad` or `squish` instead of `crop` (the default) for the initial `Resize`.\n",
+ "\n",
+ "You can see in this example the difference between an image which has been zoomed, interpolated, rotated, and then interpolated again on the right (which is the approach used by all other deep learning libraries), compared to an image which has been zoomed and rotated as one operation, and then interpolated just once on the left (the fastai approach):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "dblock1 = DataBlock(blocks=(ImageBlock(), CategoryBlock()),\n",
+ " get_y=parent_label,\n",
+ " item_tfms=Resize(460))\n",
+ "dls1 = dblock1.dataloaders([(Path.cwd()/'images'/'grizzly.jpg')]*100, bs=8)\n",
+ "dls1.train.get_idxs = lambda: Inf.ones\n",
+ "x,y = dls1.valid.one_batch()\n",
+ "_,axs = subplots(1, 2)\n",
+ "\n",
+ "x1 = TensorImage(x.clone())\n",
+ "x1 = x1.affine_coord(sz=224)\n",
+ "x1 = x1.rotate(draw=30, p=1.)\n",
+ "x1 = x1.zoom(draw=1.2, p=1.)\n",
+ "x1 = x1.warp(draw_x=-0.2, draw_y=0.2, p=1.)\n",
+ "\n",
+ "tfms = setup_aug_tfms([Rotate(draw=30, p=1, size=224), Zoom(draw=1.2, p=1., size=224),\n",
+ " Warp(draw_x=-0.2, draw_y=0.2, p=1., size=224)])\n",
+ "x = Pipeline(tfms)(x)\n",
+ "#x.affine_coord(coord_tfm=coord_tfm, sz=size, mode=mode, pad_mode=pad_mode)\n",
+ "TensorImage(x[0]).show(ctx=axs[0])\n",
+ "TensorImage(x1[0]).show(ctx=axs[1]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see here that the image on the right is less well defined, and has reflection padding artifacts in the bottom left, and the grass in the top left has disappeared entirely. We find that in practice using presizing significantly improves the accuracy of models, and often results in speedups too."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Checking and debugging a DataBlock"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can never just assume that our code is working perfectly. We know that it works for at least one filename — but we really need to check that our dataset actually makes sense. To do this, before we do any modelling, we should always use the `show_batch` method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls.show_batch(rows=1, cols=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Have a look at each image, and check that each one seems to have the correct label for that breed of pet. Often, data scientists work with data with which they are not familiar as domain experts me: for instance, I actually don't know what a lot of these pet breeds are. Since I am not an expert on pet breeds, I would use Google images at this point to search for a few of these breeds, and make sure the images looks similar to what I see in this output.\n",
+ "\n",
+ "If you made a mistake while building your `DataBlock` it is very likely you won't see it before this step. To debug this, we encourage you to use the `summary` method. It will attempt to create a batch from the source you give it, with a lot of details. Also, if it fails, you will see exactly at which point the error happens, and the library will try to give you some help. For instance, one common mistake is to forget to put a `Resize` transform, ending up with pictures of different sizes and not able to batch them. Here is what the summary would look like in that case (note that the exact text may have changed since the time of writing, but it will give you an idea):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Setting-up type transforms pipelines\n",
+ "Collecting items from /home/jhoward/.fastai/data/oxford-iiit-pet/images\n",
+ "Found 7390 items\n",
+ "2 datasets of sizes 5912,1478\n",
+ "Setting up Pipeline: PILBase.create\n",
+ "Setting up Pipeline: partial -> Categorize\n",
+ "\n",
+ "Building one sample\n",
+ " Pipeline: PILBase.create\n",
+ " starting from\n",
+ " /home/jhoward/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_31.jpg\n",
+ " applying PILBase.create gives\n",
+ " PILImage mode=RGB size=500x414\n",
+ " Pipeline: partial -> Categorize\n",
+ " starting from\n",
+ " /home/jhoward/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_31.jpg\n",
+ " applying partial gives\n",
+ " american_pit_bull_terrier\n",
+ " applying Categorize gives\n",
+ " TensorCategory(13)\n",
+ "\n",
+ "Final sample: (PILImage mode=RGB size=500x414, TensorCategory(13))\n",
+ "\n",
+ "\n",
+ "Setting up after_item: Pipeline: ToTensor\n",
+ "Setting up before_batch: Pipeline: \n",
+ "Setting up after_batch: Pipeline: IntToFloatTensor\n",
+ "\n",
+ "Building one batch\n",
+ "Applying item_tfms to the first sample:\n",
+ " Pipeline: ToTensor\n",
+ " starting from\n",
+ " (PILImage mode=RGB size=500x414, TensorCategory(13))\n",
+ " applying ToTensor gives\n",
+ " (TensorImage of size 3x414x500, TensorCategory(13))\n",
+ "\n",
+ "Adding the next 3 samples\n",
+ "\n",
+ "No before_batch transform to apply\n",
+ "\n",
+ "Collating items in a batch\n",
+ "Error! It's not possible to collate your items in a batch\n",
+ "Could not collate the 0-th members of your tuples because got the following shapes\n",
+ "torch.Size([3, 414, 500]),torch.Size([3, 375, 500]),torch.Size([3, 500, 281]),torch.Size([3, 203, 300])\n"
+ ]
+ },
+ {
+ "ename": "RuntimeError",
+ "evalue": "invalid argument 0: Sizes of tensors must match except in dimension 0. Got 414 and 375 in dimension 2 at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/TH/generic/THTensor.cpp:612",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m----------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msplitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRandomSplitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'))\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpets1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m\"images\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_fail_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Make sure all parts of your samples are tensors of the same size\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mwhy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'noop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nCollating items in a batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mcreate_batch\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa_convert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprebatched\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdo_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m else default_collate(t))\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m else default_collate(t))\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0m\u001b[1;32m 46\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m else default_collate(t))\n",
+ "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_shared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 56\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'numpy'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'str_'\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'string_'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mRuntimeError\u001b[0m: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 414 and 375 in dimension 2 at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/TH/generic/THTensor.cpp:612"
+ ]
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "pets1 = DataBlock(blocks = (ImageBlock, CategoryBlock),\n",
+ " get_items=get_image_files, \n",
+ " splitter=RandomSplitter(seed=42),\n",
+ " get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'))\n",
+ "pets1.summary(path/\"images\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "Setting-up type transforms pipelines\n",
+ "Collecting items from /home/sgugger/.fastai/data/oxford-iiit-pet/images\n",
+ "Found 7390 items\n",
+ "2 datasets of sizes 5912,1478\n",
+ "Setting up Pipeline: PILBase.create\n",
+ "Setting up Pipeline: partial -> Categorize\n",
+ "\n",
+ "Building one sample\n",
+ " Pipeline: PILBase.create\n",
+ " starting from\n",
+ " /home/sgugger/.fastai/data/oxford-iiit-pet/images/american_bulldog_83.jpg\n",
+ " applying PILBase.create gives\n",
+ " PILImage mode=RGB size=375x500\n",
+ " Pipeline: partial -> Categorize\n",
+ " starting from\n",
+ " /home/sgugger/.fastai/data/oxford-iiit-pet/images/american_bulldog_83.jpg\n",
+ " applying partial gives\n",
+ " american_bulldog\n",
+ " applying Categorize gives\n",
+ " TensorCategory(12)\n",
+ "\n",
+ "Final sample: (PILImage mode=RGB size=375x500, TensorCategory(12))\n",
+ "\n",
+ "Setting up after_item: Pipeline: ToTensor\n",
+ "Setting up before_batch: Pipeline: \n",
+ "Setting up after_batch: Pipeline: IntToFloatTensor\n",
+ "\n",
+ "Building one batch\n",
+ "Applying item_tfms to the first sample:\n",
+ " Pipeline: ToTensor\n",
+ " starting from\n",
+ " (PILImage mode=RGB size=375x500, TensorCategory(12))\n",
+ " applying ToTensor gives\n",
+ " (TensorImage of size 3x500x375, TensorCategory(12))\n",
+ "\n",
+ "Adding the next 3 samples\n",
+ "\n",
+ "No before_batch transform to apply\n",
+ "\n",
+ "Collating items in a batch\n",
+ "Error! It's not possible to collate your items in a batch\n",
+ "Could not collate the 0-th members of your tuples because got the following shapes\n",
+ "torch.Size([3, 500, 375]),torch.Size([3, 375, 500]),torch.Size([3, 333, 500]),torch.Size([3, 375, 500])\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see exactly how we gathered the data and split it, how we went from a filename to a *sample* (the tuple image, category), then what item transforms were applied and how it failed to collate those samples in a batch (because of the different shapes). \n",
+ "\n",
+ "Once you think your data looks right, we generally recommend the next step should be creating a simple model. We often see people procrastinate the training of an actual model for far too long. As a result, they don't actually get to find out what their baseline results look like. Perhaps it doesn't need lots of fancy domain specific engineering. Or perhaps the data doesn't seem to train it all. These are things that you want to know as soon as possible. So we will use the same simple model that we used in <>:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
+ "learn.fine_tune(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we've briefly discussed before, the table shown when we fit a model shows us the results after each epoch of training. Remember, an epoch is one complete pass through all of the images in the data. The columns shown are the average loss over the items of the training set, the loss on the validation set, and any metrics that you requested — in this case, the error rate.\n",
+ "\n",
+ "Remember that *loss* is whatever function we've decided to use to optimise the parameters of our model. But we haven't actually told fastai what loss function we want to use. So what is it doing? Fastai will generally try to select an appropriate loss function based on what kind of data and model you are using. In this case you have image data, and a categorical outcome, so fastai will default to using *cross entropy loss*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cross entropy loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Viewing activations and labels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Cross entropy loss* is a loss function which is similar to the loss function we used in the previous chapter, but (as we'll see) has two benefits:\n",
+ "\n",
+ "- It works even when our dependent variable has more than two categories\n",
+ "- It results in faster and more reliable training.\n",
+ "\n",
+ "In order to understand how cross entropy loss works for dependent variables with more than two categories, we first have to understand what the actual data and activations that are loss function is seen look like. To actually get a batch of real data from our DataLoaders, we can use the one_batch method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x,y = dls.one_batch()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you see, this returns the dependent, and the independent variables, as a mini-batch. Let's see what is actually contained in our dependent variable:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "TensorCategory([11, 0, 0, 5, 20, 4, 22, 31, 23, 10, 20, 2, 3, 27, 18, 23, 33, 5, 24, 7, 6, 12, 9, 11, 35, 14, 10, 15, 3, 3, 21, 5, 19, 14, 12, 15, 27, 1, 17, 10, 7, 6, 15, 23, 36, 1, 35, 6,\n",
+ " 4, 29, 24, 32, 2, 14, 26, 25, 21, 0, 29, 31, 18, 7, 7, 17], device='cuda:5')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our batch size is 64, so we have 64 rows in this tensor. Each row is a single integer between zero and 36, representing our 37 possible pet breeds. We can view the predictions (that is, the activations of the final layer of our neural network) using `Learner.get_preds`. This function either takes a dataset index (0 for train and 1 for valid) or an iterator of batches. Thus, we can pass it a simple list with our batch to get our predictions. It returns predictions and targets by default, but since we already have the targets, we can effectively ignore them by assigning to the special variable `_`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor([7.9069e-04, 6.2350e-05, 3.7607e-05, 2.9260e-06, 1.3032e-05, 2.5760e-05, 6.2341e-08, 3.6400e-07, 4.1311e-06, 1.3310e-04, 2.3090e-03, 9.9281e-01, 4.6494e-05, 6.4266e-07, 1.9780e-06, 5.7005e-07,\n",
+ " 3.3448e-06, 3.5691e-03, 3.4385e-06, 1.1578e-05, 1.5916e-06, 8.5567e-08, 5.0773e-08, 2.2978e-06, 1.4150e-06, 3.5459e-07, 1.4599e-04, 5.6198e-08, 3.4108e-07, 2.0813e-06, 8.0568e-07, 4.3381e-07,\n",
+ " 1.0069e-05, 9.1020e-07, 4.8714e-06, 1.2734e-06, 2.4735e-06])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds,_ = learn.get_preds(dl=[(x,y)])\n",
+ "preds[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The actual predictions are 37 probabilities between zero and one, which add up to 1 in total."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(37, tensor(1.0000))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(preds[0]),preds[0].sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Softmax"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "An activation function called *softmax* in the final layer is used to ensure that the activations are between zero and one, and that they sum to one.\n",
+ "\n",
+ "Softmax is similar to the sigmoid function, which we saw earlier; sigmoid looks like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.491732 | \n", + "0.337355 | \n", + "0.108254 | \n", + "00:18 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.503154 | \n", + "0.293404 | \n", + "0.096076 | \n", + "00:23 | \n", + "
| 1 | \n", + "0.314759 | \n", + "0.225316 | \n", + "0.066306 | \n", + "00:23 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_function(torch.sigmoid, min=-4,max=4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can apply this function to a single column of activations from a neural network, and get back a column of numbers between zero and one. So it's a very useful activation function for our final layer.\n",
+ "\n",
+ "Now think about what happens if we want to have more categories in our target (such as our 37 pet breeds). That means we'll need more activations than just a single column: we need an activation *per category*. We can create, for instance, a neural net that predicts \"3\"s and \"7\"s that returns two activations, one for each class--this will be a good first step towards creating the more general approach. Let's just use some random numbers with a standard deviation of 2 (so we multiply `randn` by 2) for this example, assuming we have six images and two possible categories (where the first columns represents \"3\"s and the second is \"7\"s):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "torch.random.manual_seed(42);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[ 0.6734, 0.2576],\n",
+ " [ 0.4689, 0.4607],\n",
+ " [-2.2457, -0.3727],\n",
+ " [ 4.4164, -1.2760],\n",
+ " [ 0.9233, 0.5347],\n",
+ " [ 1.0698, 1.6187]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "acts = torch.randn((6,2))*2\n",
+ "acts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can't just take the sigmoid of this directly, since we don't get rows that add to one (i.e we want the probability of being a \"3\" plus the probability of being a \"7\" to add to one):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0.6623, 0.5641],\n",
+ " [0.6151, 0.6132],\n",
+ " [0.0957, 0.4079],\n",
+ " [0.9881, 0.2182],\n",
+ " [0.7157, 0.6306],\n",
+ " [0.7446, 0.8346]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "acts.sigmoid()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In <>, the neural net created a single activation per image, which we passed through the sigmoid function. That single activation represented the confidence that the input was a \"3\". Binary problems are a special case of classification problems, because the target can be treated as a single boolean value, as we did in `mnist_loss`. Binary problems can also be thought of as part of the more general group of classifiers with any number of categories--where in this case we happen to have 2 categories. As we saw in the bear classifier, our neural net will return one activation per category.\n",
+ "\n",
+ "So in the binary case, what do those activations really indicate? A single pair of activations simply indicates the *relative* confidence of being a \"3\" versus being a \"7\". The overall values, whether they are both high, or both low, don't matter--all that matters it which is higher, and by how much.\n",
+ "\n",
+ "We would expect that since this is just another way of representing the same problem (in the binary case) that we would be able to use sigmoid directly on the two-activation version of our neural net. And indeed we can! We can just take the *difference* between the neural net activations, because that reflects how much more sure we are of being a \"3\" vs a \"7\", and then take the sigmoid of that:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0.6025, 0.5021, 0.1332, 0.9966, 0.5959, 0.3661])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(acts[:,0]-acts[:,1]).sigmoid()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The second column (the probability of being a \"7\") will then just be that subtracted from one. We need a way to do all this that also works for more than two columns. It turns out that this function, called `softmax`, is exactly that:\n",
+ "\n",
+ "``` python\n",
+ "def softmax(x): return exp(x) / exp(x).sum(dim=1, keepdim=True)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Exponential function (exp): Literally defined as `e**x`, where `e` is a special number approximately equal to 2.718. It is the inverse of the natural logarithm function. Note that `exp` is always positive, and it increases *very* rapidly!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's check that `softmax` returns the same values as `sigmoid` for the first column, and that subtracted from one for the second column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0.6025, 0.3975],\n",
+ " [0.5021, 0.4979],\n",
+ " [0.1332, 0.8668],\n",
+ " [0.9966, 0.0034],\n",
+ " [0.5959, 0.4041],\n",
+ " [0.3661, 0.6339]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sm_acts = torch.softmax(acts, dim=1)\n",
+ "sm_acts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Softmax is the multi-category equivalent of sigmoid--we have to use it any time we have more than two categories, and the probabilities of the categories must add to one. (We often use it even when there's just two categories, just to make things a bit more consistent.) We could create other functions that have the properties that all activations are between zero and one, and sum to one; however, no other function has the same relationship to the sigmoid function, which we've seen is smooth and symmetric. Also, we'll see shortly that the softmax function works well hand-in-hand with the loss function we will look at in the next section.\n",
+ "\n",
+ "If we have three output activations, such as in our bear classifier, calculating softmax for a single bear image would then look like something like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Bear](\"images/att_00062.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What does this function do in practice? Taking the exponential ensures all our numbers are positive, and then dividing by the sum ensures we are going to have a bunch of numbers that add up to one. The exponential also has a nice property: if one of the numbers in our activations `x` is slightly bigger than the others, the exponential will amplify this (since it grows, well... exponentially) which means that in the softmax, that number will be closer to 1. \n",
+ "\n",
+ "Intuitively, the Softmax function *really* wants to pick one class among the others, so it's ideal for training a classifier when we know each picture has a definite label. (Note that it may be less ideal during inference, as you might want your model to sometimes tell you it doesn't recognize any of the classes is has seen during training, and not pick a class because it has a slightly bigger activation score. In this case, it might be better to train a model using multiple binary output columns, each using a sigmoid activation.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Log likelihood"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we calculated the loss for our MNIST example in the last chapter we used.\n",
+ "\n",
+ "```python\n",
+ "def mnist_loss(inputs, targets):\n",
+ " inputs = inputs.sigmoid()\n",
+ " return torch.where(targets==1, 1-inputs, inputs).mean()\n",
+ "```\n",
+ "\n",
+ "Just like we moved from sigmoid to softmax, we need to extend the loss function to work with more than just binary classification, to classifying any number of categories (in this case, we have 37 categories). Our activations, after softmax, are between zero and one, and sum to one for each row in the batch of predictions. Our targets are integers between 0 and 36.\n",
+ "\n",
+ "In the binary case, we used `torch.where` to select between `inputs` and `1-inputs`. When we treat a binary classification as a general classification problem with two categories, it actually becomes even easier, because (as we saw in the softmax section) we now have two columns, containing the equivalent of `inputs` and `1-inputs`. So all we need to do is select from the appropriate column. Let's try to implement this in PyTorch. For our synthetic \"3\"s and \"7\" example, let's say these are our labels:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "targ = tensor([0,1,0,1,1,0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and these are the softmax activations:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0.6025, 0.3975],\n",
+ " [0.5021, 0.4979],\n",
+ " [0.1332, 0.8668],\n",
+ " [0.9966, 0.0034],\n",
+ " [0.5959, 0.4041],\n",
+ " [0.3661, 0.6339]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sm_acts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then for each item of `targ` we can use that to select that column of `sm_acts` using tensor indexing, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0.6025, 0.4979, 0.1332, 0.0034, 0.4041, 0.3661])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "idx = range(6)\n",
+ "sm_acts[idx, targ]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see exactly what's happening here, let's put all the columns together in a table. Here, the first two columns are out activations, then we have the targets, the row index, and finally the result shown immediately above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "from IPython.display import HTML\n",
+ "df = pd.DataFrame(sm_acts, columns=[\"3\",\"7\"])\n",
+ "df['targ'] = targ\n",
+ "df['idx'] = idx\n",
+ "df['loss'] = sm_acts[range(6), targ]\n",
+ "t = df.style.hide_index()\n",
+ "#To have html code compatible with our script\n",
+ "html = t._repr_html_().split('')[1]\n",
+ "html = re.sub(r'', r'', html)\n",
+ "display(HTML(html))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at this table, you can see that the final column can be calculated by taking the `targ` and `idx` columns as indices into the 2-column matrix containing the `3` and `7` columns. That's what `sm_acts[idx, targ]` is actually doing.\n",
+ "\n",
+ "The really interesting thing here is that this actually works just as well with more than two columns. To see this, consider what would happen if we added a activation column above for every digit (zero through nine), and then `targ` contained a number from zero to nine. As long as the activation columns sum to one (as they will, if we use softmax), then we'll have a loss function that shows how well we're predicting each digit.\n",
+ "\n",
+ "We're only picking the loss from the column containing the correct label. We don't to consider the other columns, because by the definition of softmax, they add up to one minus the activation corresponding to the correct label. Therefore, making the activation for the correct label as high as possible, must mean we're also decreasing the activations of the remaining columns.\n",
+ "\n",
+ "PyTorch provides a function that does exactly the same thing as `sm_acts[range(n), targ]` (except it takes the negative, since we want a smaller loss to be better), called `nll_loss` (*NLL* stands for *negative log likelihood*):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([-0.6025, -0.4979, -0.1332, -0.0034, -0.4041, -0.3661])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "-sm_acts[idx, targ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([-0.6025, -0.4979, -0.1332, -0.0034, -0.4041, -0.3661])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "F.nll_loss(sm_acts, targ, reduction='none')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Taking the `log`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This does work quite well as a loss function, but we can make it a bit better. The problem is that we are using probabilities, and probabilities cannot be smaller than zero, or greater than one. But that means that our model will not care about whether it predicts 0.99 versus 0.999, because those numbers are so close together. But in another sense, 0.999 is 10 times more confident than 0.99. So we wish to transform our numbers between zero and one to instead be between negative infinity and infinity. There is a function available in maths which does exactly this: the logarithm (available as `torch.log`). It is not defined for numbers less than zero, and looks like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What does this function do in practice? Taking the exponential ensures all our numbers are positive, and then dividing by the sum ensures we are going to have a bunch of numbers that add up to one. The exponential also has a nice property: if one of the numbers in our activations `x` is slightly bigger than the others, the exponential will amplify this (since it grows, well... exponentially) which means that in the softmax, that number will be closer to 1. \n",
+ "\n",
+ "Intuitively, the Softmax function *really* wants to pick one class among the others, so it's ideal for training a classifier when we know each picture has a definite label. (Note that it may be less ideal during inference, as you might want your model to sometimes tell you it doesn't recognize any of the classes is has seen during training, and not pick a class because it has a slightly bigger activation score. In this case, it might be better to train a model using multiple binary output columns, each using a sigmoid activation.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Log likelihood"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we calculated the loss for our MNIST example in the last chapter we used.\n",
+ "\n",
+ "```python\n",
+ "def mnist_loss(inputs, targets):\n",
+ " inputs = inputs.sigmoid()\n",
+ " return torch.where(targets==1, 1-inputs, inputs).mean()\n",
+ "```\n",
+ "\n",
+ "Just like we moved from sigmoid to softmax, we need to extend the loss function to work with more than just binary classification, to classifying any number of categories (in this case, we have 37 categories). Our activations, after softmax, are between zero and one, and sum to one for each row in the batch of predictions. Our targets are integers between 0 and 36.\n",
+ "\n",
+ "In the binary case, we used `torch.where` to select between `inputs` and `1-inputs`. When we treat a binary classification as a general classification problem with two categories, it actually becomes even easier, because (as we saw in the softmax section) we now have two columns, containing the equivalent of `inputs` and `1-inputs`. So all we need to do is select from the appropriate column. Let's try to implement this in PyTorch. For our synthetic \"3\"s and \"7\" example, let's say these are our labels:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "targ = tensor([0,1,0,1,1,0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and these are the softmax activations:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0.6025, 0.3975],\n",
+ " [0.5021, 0.4979],\n",
+ " [0.1332, 0.8668],\n",
+ " [0.9966, 0.0034],\n",
+ " [0.5959, 0.4041],\n",
+ " [0.3661, 0.6339]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sm_acts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then for each item of `targ` we can use that to select that column of `sm_acts` using tensor indexing, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0.6025, 0.4979, 0.1332, 0.0034, 0.4041, 0.3661])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "idx = range(6)\n",
+ "sm_acts[idx, targ]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see exactly what's happening here, let's put all the columns together in a table. Here, the first two columns are out activations, then we have the targets, the row index, and finally the result shown immediately above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 3 | 7 | targ | idx | loss |
|---|---|---|---|---|
| 0.602469 | \n", + "0.397531 | \n", + "0 | \n", + "0 | \n", + "0.602469 | \n", + "
| 0.502065 | \n", + "0.497935 | \n", + "1 | \n", + "1 | \n", + "0.497935 | \n", + "
| 0.133188 | \n", + "0.866811 | \n", + "0 | \n", + "2 | \n", + "0.133188 | \n", + "
| 0.99664 | \n", + "0.00336017 | \n", + "1 | \n", + "3 | \n", + "0.00336017 | \n", + "
| 0.595949 | \n", + "0.404051 | \n", + "1 | \n", + "4 | \n", + "0.404051 | \n", + "
| 0.366118 | \n", + "0.633882 | \n", + "0 | \n", + "5 | \n", + "0.366118 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "8.946717 | \n", + "47.954632 | \n", + "0.893775 | \n", + "00:20 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "7.231843 | \n", + "4.119265 | \n", + "0.954668 | \n", + "00:24 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
+ "lr_min,lr_steep = learn.lr_find()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Minimum/10: 8.32e-03, steepest point: 6.31e-03\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"Minimum/10: {lr_min:.2e}, steepest point: {lr_steep:.2e}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see on this plot that in the range 1e-6 to 1e-3, nothing really happens and the model doesn't train. Then the loss starts to decrease until it reaches a minimum then increases again. We don't want a learning rate greater than 1e-1 as it will give a training that diverges (you can try for yourself) but 1e-1 is already too high: at this stage we left the period where the loss was decreasing steadily.\n",
+ "\n",
+ "In this learning rate plot it appears that a learning rate around 3e-3 would be appropriate, so let's choose that."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> Note: The learning rate finder plot has a logarithmic scale, which is why the middle point between 1e-3 and 1e-2 is between 3e-3 and 4e-3. This is because we care mostly about the order of magnitude of the learning rate."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
+ "learn.fine_tune(2, base_lr=3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Something really interesting about this is that it was only discovered in 2015. Neural networks have been under development since the 1950s. Throughout that time finding a good learning rate has been, perhaps, the most important and challenging issue for practitioners. The idea does not require any advanced maths, giant computing resources, huge datasets, or anything else that would make it inaccessible to any curious researcher. Furthermore, the researcher who did develop it, Leslie Smith, was not part of some exclusive Silicon Valley lab, but was working as a naval researcher. All of this is to say: breakthrough work in deep learning absolutely does not require access to vast resources, elite teams, or advanced mathematical ideas. There is lots of work still to be done which requires just a bit of common sense, creativity, and tenacity."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Unfreezing and transfer learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We discussed briefly in <> how transfer learning works. We saw that the basic idea is that a pretrained model, trained potentially on millions of data points (such as ImageNet), is fine tuned for some other task. But what does this really mean?\n",
+ "\n",
+ "We now know that a convolutional neural network consists of many layers with a non-linear activation function between each and one or more final linear layers, with an activation functions such as softmax at the very end. The final linear layer uses a matrix with enough columns such that the output size is the same as the number of classes in our model (assuming that we are doing classification).\n",
+ "\n",
+ "This final linear layer is unlikely to be of any use for us, when we are fine tuning in a transfer learning setting, because it is specifically designed to classify the categories in the original pretraining dataset. So when we do transfer learning we remove it, and throw it away, and replace it with a new linear layer with the correct number of outputs for our desired task (in this case, there would be 37 activations).\n",
+ "\n",
+ "This newly added linear layer will have entirely random weights. Therefore, our model prior to fine tuning has entirely random outputs. But that does not mean that it is an entirely random model! All of the layers prior to the last one have been carefully trained to be good at image classification tasks in general. As we saw in the images from the Zeiler and Fergus paper, the first layers encode very general concepts such as finding gradients and edges, and later layers encode concepts that are still very useful for us, such as finding eyeballs and fur.\n",
+ "\n",
+ "We want to train a model in such a way that we allow it to remember all of these generally useful ideas from the pretrained model, use them to solve our particular task (classify pet breeds), and only adjust them as required for the specifics of our particular task.\n",
+ "\n",
+ "Our challenge than when fine tuning is to replace the random weights in our added linear layers with weights that correctly achieve our desired task (classifying pet breeds) without breaking the carefully pretrained weights and the other layers. There is actually a very simple trick to allow this to happen: tell the optimiser to only update the weights in those randomly added final layers. Don't change the weights in the rest of the neural network at all. This is called *freezing* those pretrained layers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we create a model from a pretrained network fastai automatically freezes all of the pretrained layers for us. When we call the `fine_tune` method fastai does two things:\n",
+ "\n",
+ "- train the randomly added layers for one epoch, with all other layers frozen ;\n",
+ "- unfreeze all of the layers, and train them all for the number of epochs requested.\n",
+ "\n",
+ "Although this is a reasonable default approach, it is likely that for your particular dataset you may get better results by doing things slightly differently. The `fine_tune` method has a number of parameters you can use to change its behaviour, but it might be easiest for you to just call the underlying methods directly. Remember that you can see the source code for the method by using the following syntax:\n",
+ "\n",
+ " learn.fine_tune??\n",
+ "\n",
+ "So let's try doing this manually ourselves. First of all we will train the randomly added layers for three epochs, using `fit_one_cycle`. As mentioned in <>, `fit_one_cycle` is the suggested way to train models without using `fine_tune`. We'll see why later in the book; in short, what `fit_one_cycle` does is to start training at a low learning rate, gradually increase it for the first section of training, and then gradually decrease it again for the last section of training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
+ "learn.fit_one_cycle(3, 3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And then we will unfreeze the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.unfreeze()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and run `lr_find` again, because having more layers to train, and weights that have already been trained for 3 epochs, means our previously found learning rate isn't appropriate and more:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(1.0964782268274575e-05, 1.5848931980144698e-06)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.071820 | \n", + "0.427476 | \n", + "0.133965 | \n", + "00:19 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.738273 | \n", + "0.541828 | \n", + "0.150880 | \n", + "00:24 | \n", + "
| 1 | \n", + "0.401544 | \n", + "0.266623 | \n", + "0.081867 | \n", + "00:24 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.188042 | \n", + "0.355024 | \n", + "0.102842 | \n", + "00:20 | \n", + "
| 1 | \n", + "0.534234 | \n", + "0.302453 | \n", + "0.094723 | \n", + "00:20 | \n", + "
| 2 | \n", + "0.325031 | \n", + "0.222268 | \n", + "0.074425 | \n", + "00:20 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.lr_find()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the graph is a little different from when we had random weights: we don't have that sharp descent that indicates the model is training. That's because our model has been trained already. Here we have a somewhat flat area before a sharp increase, and we should take a point well before that sharp increase, for instance 1e-5. The point with the maximum gradient isn't what we look for here and should be ignored.\n",
+ "\n",
+ "Let's train at a suitable learning rate:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(6, lr_max=1e-5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This has improved our model a bit, but there's more we can do..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Discriminative learning rates"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Even after we unfreeze, we still care a lot about the quality of those pretrained weights. We would not expect that the best learning rate for those pretrained parameters would be as high as the randomly added parameters — even after we have tuned those randomly added parameters for a few epochs. Remember, the pretrained weights have been trained for hundreds of epochs, on millions of images.\n",
+ "\n",
+ "In addition, do you remember the images we saw in <>, showing what each layer learns? The first layer learns very simple foundations, like edge and gradient detectors; these are likely to be just as useful for nearly any task. The later layers learn much more complex concepts, like \"eye\" and \"sunset\", which might not be useful in your task at all (maybe you're classifying car models, for instance). So it makes sense to let the later layers fine-tune more quickly than earlier layers.\n",
+ "\n",
+ "Therefore, fastai by default does something called *discriminative learning rates*. This was originally developed in the ULMFiT approach to NLP transfer learning that we introduced in <>. Like many good ideas in deep learning, it is extremely simple: use a lower learning rate for the early layers of the neural network, and a higher learning rate for the later layers (and especially the randomly added layers). The idea is based on insights developed by Jason Yosinski, who showed in 2014 that when transfer learning different layers of a neural network should train at different speeds:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Impact](\"images/att_00039.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fastai lets you pass a Python *slice* object anywhere that a learning rate is expected. The first value past will be the learning rate in the earliest layer of the neural network, and the second value will be the learning rate in the final layer. The layers in between will have learning rates that are multiplicatively equidistant throughout that range. Let's use this approach to replicate the previous training, but this time we'll only set the *lowest* layer of our net to a learning rate of `1e-6`; the other layers will scale up to `1e-4`. Let's train for a while and see what happens."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
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+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
+ "learn.fit_one_cycle(3, 3e-3)\n",
+ "learn.unfreeze()\n",
+ "learn.fit_one_cycle(12, lr_max=slice(1e-6,1e-4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now the fine tuning is working great!\n",
+ "\n",
+ "Fastai can show us a graph of the training and validation loss:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.263579 | \n", + "0.217419 | \n", + "0.069012 | \n", + "00:24 | \n", + "
| 1 | \n", + "0.253060 | \n", + "0.210346 | \n", + "0.062923 | \n", + "00:24 | \n", + "
| 2 | \n", + "0.224340 | \n", + "0.207357 | \n", + "0.060217 | \n", + "00:24 | \n", + "
| 3 | \n", + "0.200195 | \n", + "0.207244 | \n", + "0.061570 | \n", + "00:24 | \n", + "
| 4 | \n", + "0.194269 | \n", + "0.200149 | \n", + "0.059540 | \n", + "00:25 | \n", + "
| 5 | \n", + "0.173164 | \n", + "0.202301 | \n", + "0.059540 | \n", + "00:25 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fastai lets you pass a Python *slice* object anywhere that a learning rate is expected. The first value past will be the learning rate in the earliest layer of the neural network, and the second value will be the learning rate in the final layer. The layers in between will have learning rates that are multiplicatively equidistant throughout that range. Let's use this approach to replicate the previous training, but this time we'll only set the *lowest* layer of our net to a learning rate of `1e-6`; the other layers will scale up to `1e-4`. Let's train for a while and see what happens."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.145300 | \n", + "0.345568 | \n", + "0.119756 | \n", + "00:20 | \n", + "
| 1 | \n", + "0.533986 | \n", + "0.251944 | \n", + "0.077131 | \n", + "00:20 | \n", + "
| 2 | \n", + "0.317696 | \n", + "0.208371 | \n", + "0.069012 | \n", + "00:20 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.257977 | \n", + "0.205400 | \n", + "0.067659 | \n", + "00:25 | \n", + "
| 1 | \n", + "0.246763 | \n", + "0.205107 | \n", + "0.066306 | \n", + "00:25 | \n", + "
| 2 | \n", + "0.240595 | \n", + "0.193848 | \n", + "0.062246 | \n", + "00:25 | \n", + "
| 3 | \n", + "0.209988 | \n", + "0.198061 | \n", + "0.062923 | \n", + "00:25 | \n", + "
| 4 | \n", + "0.194756 | \n", + "0.193130 | \n", + "0.064276 | \n", + "00:25 | \n", + "
| 5 | \n", + "0.169985 | \n", + "0.187885 | \n", + "0.056157 | \n", + "00:25 | \n", + "
| 6 | \n", + "0.153205 | \n", + "0.186145 | \n", + "0.058863 | \n", + "00:25 | \n", + "
| 7 | \n", + "0.141480 | \n", + "0.185316 | \n", + "0.053451 | \n", + "00:25 | \n", + "
| 8 | \n", + "0.128564 | \n", + "0.180999 | \n", + "0.051421 | \n", + "00:25 | \n", + "
| 9 | \n", + "0.126941 | \n", + "0.186288 | \n", + "0.054127 | \n", + "00:25 | \n", + "
| 10 | \n", + "0.130064 | \n", + "0.181764 | \n", + "0.054127 | \n", + "00:25 | \n", + "
| 11 | \n", + "0.124281 | \n", + "0.181855 | \n", + "0.054127 | \n", + "00:25 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.recorder.plot_loss()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, the training loss keeps getting better and better. But notice that eventually the validation loss improvement slows, and sometimes even gets worse! This is the point at which the model is starting to over fit. In particular, the model is becoming overconfident of its predictions. But this does *not* mean that it is getting less accurate, necessarily. Have a look at the table of training results per epoch, and you will often see that the accuracy continues improving, even as the validation loss gets worse. In the end what matters is your accuracy, or more generally your chosen metrics, not the loss. The loss is just the function we've given the computer to help us to optimise."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Selecting the number of epochs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Often you will find that you are limited by time, rather than generalisation and accuracy, when choosing how many epochs to train for. So your first approach to training should be to simply pick a number of epochs that will train in the amount of time that you are happy to wait for. Have a look at the training and validation loss plots, likely showed above, and in particular your metrics, and if you see that they are still getting better even in your final epochs, then you know that you have not trained for too long.\n",
+ "\n",
+ "On the other hand, you may well see that the metrics you have chosen are really getting worse at the end of training. Remember, it's not just that were looking for the validation loss to get worse, but your actual metrics. Your validation loss will first of all during training get worse because it gets overconfident, and only later will get worse because it is incorrectly memorising the data. We only care in practice about the latter issue. Our loss function is just something, remember, that we used to allow our optimiser to have something it could differentiate and optimise; it's not actually the thing we care about in practice.\n",
+ "\n",
+ "Before the days of 1cycle training it was very common to save the model at the end of each epoch, and then select whichever model had the best accuracy, out of all of the models saved in each epoch. This is known as *early stopping*. However, with one cycle training, it is very unlikely to give you the best answer, because those epochs in the middle occur before the learning rate has had a chance to reach the small values, where it can really find the best result. Therefore, if you find that you have overfit, what you should actually do is to retrain your model from scratch, and this time select a total number of epochs based on where your previous best results were found."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Deeper architectures"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we've got the time to train for more epochs, we may want to instead use that time to train more parameters. In general, a model with more parameters can model your data more accurately. (There are lots and lots of caveats to this generalisation, and it depends on the specifics of the architectures you are using, but it is a reasonable rule of thumb for now.) For most of the architectures that we will be seeing in this book you can create larger versions of them by simply adding more layers. However, since we want to use pretrained models, we need to make sure that we choose a number of layers that has been already pretrained for us.\n",
+ "\n",
+ "This is why, in practice, architectures tend to come in a small number of variants. For instance, the resnet architecture that we are using in this chapter comes in 18, 34, 50, 101, and 152 layer variants, pre-trained on ImageNet. A larger (more layers and parameters; sometimes described as the \"capacity\" of a model) version of a resnet will always be able to give us a better training loss, but it can suffer more from overfitting, because it has more parameters to over fit with.\n",
+ "\n",
+ "In general, a bigger model has the ability to better capture the real underlying relationships in your data, and also to capture and memorise the specific details of your individual images.\n",
+ "\n",
+ "However, using a deeper model is going to require more GPU RAM, so we may need to lower the size of our batches to avoid *out-of-memory errors*. This happens when you try to fit too much inside your GPU and looks like:\n",
+ "\n",
+ "```\n",
+ "Cuda runtime error: out of memory\n",
+ "```\n",
+ "\n",
+ "You may have to restart your notebook when this happens, and the way to solve it is to use a smaller *batch size*, which means we will pass smaller groups of images at any given time through our model. We can pass the batch size we want to the call creating our `DataLoaders` with `bs=`.\n",
+ "\n",
+ "The other downside of deeper architectures is that they take quite a bit longer to train. One thing that can speed things up a lot is *mixed precision training*. This refers to using less precise numbers (*half precision floating point*, also called *fp16*) where possible during training. As we are writing this words (early 2020) nearly all current NVIDIA GPUs support a special feature called *tensor cores* which can dramatically (2x-3x) speed up neural network training. They also require a lot less GPU memory. To enable this feature in fastai, just add `to_fp16()` after your `Learner` creation (you also need to import the module).\n",
+ "\n",
+ "You can't really know ahead of time what the best architecture for your particular problem is, until you try training some. So let's try a resnet 50 now with mixed precision:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from fastai2.callback.fp16 import *\n",
+ "learn = cnn_learner(dls, resnet50, metrics=error_rate).to_fp16()\n",
+ "learn.fine_tune(6, freeze_epochs=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You'll see here we've gone back to using `fine_tune`, since it's so handy! We can pass `freeze_epochs` to tell fastai how many epochs to train for while frozen. It will automatically change learning rates appropriately for most datasets.\n",
+ "\n",
+ "In this case, we're not seeing a clear win from the deeper model. This is useful to remember--bigger models aren't necessarily better models for your particular case! Make sure you try small models before you start scaling up."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summary"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this chapter we learned some important practical tips, both for getting our image data ready for modeling (presizing; data block summary) and for fitting the model (learning rate finder, unfreezing, discriminative learning rates, setting the number of epochs, and using deeper architectures). Using these tools will help you to build more accurate image models, more quickly.\n",
+ "\n",
+ "We also learned about cross entropy loss. This part of the book is worth spending plenty of time on. You aren't likely to need to actually implement cross entropy loss from scratch yourself in practice, but it's really important you understand the inputs to and output from that function, because it (or a variant of it, as we'll see in the next chapter) is used in nearly everything classification model. So when you want to debug a model, or put a model in production, or improve the accuracy of a model, you're going to need to be able to look at its activations and loss, and understand what's going on, and why. You can't do that properly if you don't understand your loss function.\n",
+ "\n",
+ "If cross entropy loss hasn't \"clicked\" for you just yet, don't worry--you'll get there! First, go back to the last chapter and make sure you really understand `mnist_loss`. Then work gradually through the cells of the notebook for this chapter, where we step through each piece of cross entropy loss. Make sure you understand what each calculation is doing, and why. Try creating some small tensors yourself and pass them into the functions, to see what they return.\n",
+ "\n",
+ "Remember: the choices made in cross entropy loss are not the only possible choices that could have been made. Just like when we looked at regression, we could choose between mean squared error and mean absolute difference (L1), we could change the details inside cross entropy loss too. If you have other ideas for possible functions that you think might work, feel free to give them a try in this chapter's notebook! (Fair warning though: you'll probably find that the model will be slower to train, and less accurate. That's because the gradient of cross entropy loss is proportional to the difference between the activation and the target, so SGD always gets a nicely scaled step for the weights.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Why do we first resize to a large size on the CPU, and then to a smaller size on the GPU?\n",
+ "1. If you are not familiar with regular expressions, find a regular expression tutorial, and some problem sets, and complete them. Have a look on the book website for suggestions.\n",
+ "1. What are the two ways in which data is most commonly provided, for most deep learning datasets?\n",
+ "1. Look up the documentation for `L` and try using a few of the new methods is that it adds.\n",
+ "1. Look up the documentation for the Python pathlib module and try using a few methods of the Path class.\n",
+ "1. Give two examples of ways that image transformations can degrade the quality of the data.\n",
+ "1. What method does fastai provide to view the data in a DataLoader?\n",
+ "1. What method does fastai provide to help you debug a DataBlock?\n",
+ "1. Should you hold off on training a model until you have thoroughly cleaned your data?\n",
+ "1. What are the two pieces that are combined into cross entropy loss in PyTorch?\n",
+ "1. What are the two properties of activations that softmax ensures? Why is this important?\n",
+ "1. When might you want your activations to not have these two properties?\n",
+ "1. Calculate the \"exp\" and \"softmax\" columns of <> yourself (i.e. in a spreadsheet, with a calculator, or in a notebook).\n",
+ "1. Why can't we use torch.where to create a loss function for datasets where our label can have more than two categories?\n",
+ "1. What is the value of log(-2)? Why?\n",
+ "1. What are two good rules of thumb for picking a learning rate from the learning rate finder?\n",
+ "1. What two steps does the fine_tune method do?\n",
+ "1. In Jupyter notebook, how do you get the source code for a method or function?\n",
+ "1. What are discriminative learning rates?\n",
+ "1. How is a Python slice object interpreted when past as a learning rate to fastai?\n",
+ "1. Why is early stopping a poor choice when using one cycle training?\n",
+ "1. What is the difference between resnet 50 and resnet101?\n",
+ "1. What does to_fp16 do?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Find the paper by Leslie Smith that introduced the learning rate finder, and read it.\n",
+ "1. See if you can improve the accuracy of the classifier in this chapter. What's the best accuracy you can achieve? Have a look on the forums and book website to see what other students have achieved with this dataset, and how they did it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06_multicat.ipynb b/06_multicat.ipynb
new file mode 100644
index 000000000..e0a107eb0
--- /dev/null
+++ b/06_multicat.ipynb
@@ -0,0 +1,1905 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_multicat]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Other computer vision problems"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the previous chapter we learnt some important practical techniques for training models in practice. Issues like selecting learning rates and the number of epochs are very important to getting good results.\n",
+ "\n",
+ "In this chapter we are going to look at other types of computer vision problems, multi-label classification and regression. In the process will study more deeply the output activations, targets, and loss functions in deep learning models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multi-label classification"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Multi-label classification refers to the problem of identifying the categories of objects in an image, where you may not have exactly one type of object in the image. There may be more than one kind of object, or there may be no objects at all in the classes that you are looking for.\n",
+ "\n",
+ "For instance, this would have been a great approach for our bear classifier. One problem with the bear classifier that we rolled out before is that if a user uploaded something that wasn't any kind of bear, the model would still say it was either a grizzly, black, or teddy bear — it had no ability to predict \"not a bear at all\". In fact, after we have completed this chapter, it would be a great exercise for you to go back to your image classifier application, and try to retrain it using the multi-label technique. And then, tested by passing in an image which is not of any of your recognised classes.\n",
+ "\n",
+ "In practice, we have not seen many examples of people training multi-label classifiers for this purpose. But we very often see both users and developers complaining about this problem. It appears that this simple solution is not at all widely understood or appreciated. Because in practice it is probably more common to have some images with zero matches or more than one match, we should probably expect in practice that multi-label classifiers are more widely applicable than single label classifiers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For our example we are going to use the *Pascal* dataset, which can have more than one kind of classified object per image.\n",
+ "\n",
+ "We begin by downloading and extracting the dataset as per usual:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.vision.all import *\n",
+ "path = untar_data(URLs.PASCAL_2007)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This dataset is different to the ones we have seen before, and that it is not structured by file name or folder, but instead comes with a CSV (comma separated values) file telling us what labels to use for each image. We can have a look at the CSV file by reading it into a Pandas DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.427505 | \n", + "0.310554 | \n", + "0.098782 | \n", + "00:21 | \n", + "
| 1 | \n", + "0.606785 | \n", + "0.302325 | \n", + "0.094723 | \n", + "00:22 | \n", + "
| 2 | \n", + "0.409267 | \n", + "0.294803 | \n", + "0.091340 | \n", + "00:21 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "error_rate | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.261121 | \n", + "0.274507 | \n", + "0.083897 | \n", + "00:26 | \n", + "
| 1 | \n", + "0.296653 | \n", + "0.318649 | \n", + "0.084574 | \n", + "00:26 | \n", + "
| 2 | \n", + "0.242356 | \n", + "0.253677 | \n", + "0.069012 | \n", + "00:26 | \n", + "
| 3 | \n", + "0.150684 | \n", + "0.251438 | \n", + "0.065629 | \n", + "00:26 | \n", + "
| 4 | \n", + "0.094997 | \n", + "0.239772 | \n", + "0.064276 | \n", + "00:26 | \n", + "
| 5 | \n", + "0.061144 | \n", + "0.228082 | \n", + "0.054804 | \n", + "00:26 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " fname labels is_valid\n",
+ "0 000005.jpg chair True\n",
+ "1 000007.jpg car True\n",
+ "2 000009.jpg horse person True\n",
+ "3 000012.jpg car False\n",
+ "4 000016.jpg bicycle True"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv(path/'train.csv')\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that the list of categories in each image is shown as a space delimited string."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: Pandas and DataFrames"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "No, it’s not actually a panda! *Pandas* is a Python library that is used to manipulate and analysis tabular and timeseries data. The main class is `DataFrame`, which represents a table of rows and columns. You can get a DataFrame from a CSV file, a database table, python dictionaries, and many other sources. In Jupyter, a DataFrame is output as a formatted table, as you see above.\n",
+ "\n",
+ "You can access rows and columns of a DataFrame with the `iloc` property, which lets you access rows and columns as if it is a matrix:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "fname 000005.jpg\n",
+ "labels chair\n",
+ "is_valid True\n",
+ "Name: 0, dtype: object"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.iloc[:,0]\n",
+ "df.iloc[0,:]\n",
+ "# Trailing ‘:’s are always optional (in numpy, PyTorch, pandas, etc),\n",
+ "# so this is equivalent:\n",
+ "df.iloc[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also grab a column by name by indexing into a DataFrame directly:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 000005.jpg\n",
+ "1 000007.jpg\n",
+ "2 000009.jpg\n",
+ "3 000012.jpg\n",
+ "4 000016.jpg\n",
+ " ... \n",
+ "5006 009954.jpg\n",
+ "5007 009955.jpg\n",
+ "5008 009958.jpg\n",
+ "5009 009959.jpg\n",
+ "5010 009961.jpg\n",
+ "Name: fname, Length: 5011, dtype: object"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['fname']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can create new columns and do calculations using columns:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TK"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pandas is a fast and flexible library, and is an important part of every data scientist’s Python toolbox. Unfortunately, its API can be rather confusing and surprising, so it takes a while to get familiar with it. If you haven’t used Pandas before, we’d suggest going through a tutorial; we are particularly fond of the book “*Python for Data Analysis*” by Wes McKinney, the creator of Pandas. It also covers other important libraries like matplotlib and numpy. We will try to briefly describe Pandas functionality we use as we come across it, but will not go into the level of detail of McKinney’s book."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Constructing a data block"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "How do we convert from a `DataFrame` object to a `DataLoaders` object? We generally suggest using the data block API for creating a `DataLoaders` object, where possible, since it provides a good mix of flexibility and simplicity. Here we will show you the steps that we take to use the data blocks API to construct a `DataLoaders` object in practice, using this dataset as an example.\n",
+ "\n",
+ "As we have seen, PyTorch and fastai have two main classes for representing and accessing a training set or validation set:\n",
+ "\n",
+ "- `Dataset`: a collection which returns a tuple of your independent and dependent variable for a single item\n",
+ "- `DataLoader`: an iterator which provides a stream of mini batches, where each mini batch is a couple of a batch of independent variables and a batch of dependent variables\n",
+ "\n",
+ "On top of these, fastai provides two classes for bringing your training and validation sets together:\n",
+ "\n",
+ "- `Datasets`: an object which contains a training `Dataset` and a validation `Dataset`\n",
+ "- `DataLoaders`: an object which contains a training `DataLoader` and a validation `DataLoader`\n",
+ "\n",
+ "Since a `DataLoader` builds on top of a `Dataset`, and adds additional functionality to it (collating multiple items into a mini batch), it’s often easiest to start by creating and testing `Datasets`, and then look at `DataLoaders` after that’s working.\n",
+ "\n",
+ "When we create a `DataBlock`, we build up gradually, step-by-step, and use the notebook to check our data along the way. This is a great way to make sure that you maintain momentum as you are coding, and that you keep an eye out for any problems. It’s easy to debug, because you know that if there are any problems, it is in the line of code you just typed!\n",
+ "\n",
+ "Let’s start with the simplest case, which is a data block created with no parameters:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dblock = DataBlock()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create a `Datasets` object from this. The only thing needed is a source, in this case, our dataframe:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dsets = dblock.datasets(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "this contains a `train` and a “valid” dataset, which we can index into:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(fname 008663.jpg\n",
+ " labels car person\n",
+ " is_valid False\n",
+ " Name: 4346, dtype: object, fname 008663.jpg\n",
+ " labels car person\n",
+ " is_valid False\n",
+ " Name: 4346, dtype: object)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dsets.train[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, this simply returns a row of the dataframe, twice. This is because by default, the datablock assumes we have two things: input and target. We are going to need to grab the appropriate fields from the DataFrame, which we can do by passing `get_x` and `get_y` functions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('005620.jpg', 'aeroplane')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dblock = DataBlock(get_x = lambda r: r['fname'], get_y = lambda r: r['labels'])\n",
+ "dsets = dblock.datasets(df)\n",
+ "dsets.train[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, rather than defining a function in the usual way, we are using Python’s *lambda* keyword. This is just a shortcut for defining and then referring to a function. The above is identical to the following more verbose approach:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('002549.jpg', 'tvmonitor')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def get_x(r): return r['fname']\n",
+ "def get_y(r): return r['labels']\n",
+ "dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
+ "dsets = dblock.datasets(df)\n",
+ "dsets.train[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "lambda functions are great for quickly iterating, however they are not compatible with serialization, so we advise you to use the more verbose approach if you want to export your `Learner` after training (they are fine if you are just experimenting)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that the independent variable will need to be converted into a complete path, so that we can open it as an image, and the second will need to be split on the space character (which is the default for Python’s split function) so that it becomes a list:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(Path('train/002844.jpg'), ['train'])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def get_x(r): return path/'train'/r['fname']\n",
+ "def get_y(r): return r['labels'].split(' ')\n",
+ "dblock = DataBlock(get_x = get_x, get_y = get_y)\n",
+ "dsets = dblock.datasets(df)\n",
+ "dsets.train[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To actually open the image and do the conversion to tensors, we will need to use a set of transforms; block types will provide us with those. We can use the same block types that we have used previously, with one exception. The `ImageBlock` will work fine again, because we have a path which points to a valid image, but the `CategoryBlock` is not going to work. The problem is: that block returns a single integer. But we need to be able to have multiple labels for each item. To solve this, we use a `MultiCategoryBlock`. This type of block expects to receive a list of strings, as we have in this case, so let’s test it out:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(PILImage mode=RGB size=500x375,\n",
+ " TensorMultiCategory([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
+ " get_x = get_x, get_y = get_y)\n",
+ "dsets = dblock.datasets(df)\n",
+ "dsets.train[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, our list of categories is not encoded in the same way that it was for the regular CategoryBlock. In that case, we had a single integer, representing which category was present, based on its location in our vocab. In this case, however, we instead have a list of zeros, with a one in any position where that category is present. For example, if there is a one in the second and fourth positions, then that means that vocab items two and four are present in this image. This is known as *one hot encoding*. The reason we can’t easily just use a list of category indices, is that each list would be a different length, and PyTorch requires tensors, where everything has to be the same length."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: One hot encoding: using a vector of zeros, with a one in each location that is represented in the data, to encode a list of integers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let’s check what the categories represent for this example (we are using the convenient torch.where function, which tells us all of the indices where our condition is true or false):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#1) ['dog']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "idxs = torch.where(dsets.train[0][1]==1.)[0]\n",
+ "dsets.train.vocab[idxs]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With numpy arrays, PyTorch tensors, and fastai’s L class, you can index directly using a list or vector, which makes a lot of code (such as this example) much clearer and more concise.\n",
+ "\n",
+ "We have ignored the column `is_valid` up until now, which means that `DataBlock` has been using a random split by default. To explicitly choose the elements of our validation set, we need to write a function and pass it to `splitter` (or use one of fastai's predefined functions or classes). It will take the items (here our whole dataframe) and must return two (or more) list of integers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(PILImage mode=RGB size=500x333,\n",
+ " TensorMultiCategory([0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def splitter(df):\n",
+ " train = df.index[~df['is_valid']].tolist()\n",
+ " valid = df.index[df['is_valid']].tolist()\n",
+ " return train,valid\n",
+ "\n",
+ "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
+ " splitter=splitter,\n",
+ " get_x=get_x, \n",
+ " get_y=get_y)\n",
+ "\n",
+ "dsets = dblock.datasets(df)\n",
+ "dsets.train[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we have discussed, a `DataLoader` collates the items from a `Dataset` into a mini batch. This is a tuple of tensors, where each tensor simply stacks the items from that location in the `Dataset` item. Now that we have confirmed that the individual items look okay there's one more step we need to ensure we can create our `DataLoaders`, which is to ensure that every item is of the same size. To do this, we can use `RandomResizedCrop`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n",
+ " splitter=splitter,\n",
+ " get_x=get_x, \n",
+ " get_y=get_y,\n",
+ " item_tfms = RandomResizedCrop(128, min_scale=0.35))\n",
+ "dls = dblock.dataloaders(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now we can display a sample of our data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | fname | \n", + "labels | \n", + "is_valid | \n", + "
|---|---|---|---|
| 0 | \n", + "000005.jpg | \n", + "chair | \n", + "True | \n", + "
| 1 | \n", + "000007.jpg | \n", + "car | \n", + "True | \n", + "
| 2 | \n", + "000009.jpg | \n", + "horse person | \n", + "True | \n", + "
| 3 | \n", + "000012.jpg | \n", + "car | \n", + "False | \n", + "
| 4 | \n", + "000016.jpg | \n", + "bicycle | \n", + "True | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls.show_batch(rows=1, cols=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And remember that if anything goes wrong when you create your `DataLoaders` from your `DataBlock`, or if you want to view exactly what happens with your `DataBlock`, you can use the `summary` method we presented in the last chapter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Binary cross entropy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we'll create our `Learner`. We saw in <> that a `Learner` object contains four main things: the model, a `DataLoaders` object, an `Optimizer`, and the loss function to use. We already how our `DataLoaders`, and we can leverage fastai's `resnet` models (which we'll learn how to create from scratch later), and we know how to create an `SGD` optimizer. So let's focus on ensuring we have a suitable loss function. To do this, let's use `cnn_learner` to create a `Learner`, so we can look at its activations:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = cnn_learner(dls, resnet18)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also saw that the model in a `Learner` is generally an object of a class inheriting from `nn.Module`, and that you can call it using parentheses and it will return the activations of a model. You should pass it your independent variable, as a mini batch. We can try it out by grabbing a mini batch from our `DataLoader`, and then passing it to the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 20])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x,y = dls.train.one_batch()\n",
+ "activs = learn.model(x)\n",
+ "activs.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Have a think about why `activs` has this shape… We have a batch size of 64. And we need to calculate the probability of each of 20 categories. Here’s what one of those activations looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([-1.0028, 0.3400, -0.5906, 0.7806, 3.1160, -0.1994, 1.3180, 1.6361, -1.7553, 0.2217, 2.8052, 1.3229, 0.9369, -1.4760, -0.3204, -2.3116, -3.8615, -1.5931, 0.0745, -3.6006],\n",
+ " device='cuda:5', grad_fn=)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "activs[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: Knowing how to manually get a mini batch and pass it into a model, and look at the activations and loss, is really important for debugging your model. It is also very helpful for learning, so that you can see exactly what is going on."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "They aren’t yet scaled between zero and one. We learned in <> how to scale activations to be between zero and one: the `sigmoid` function. We also saw how to calculate a loss based on this--this is our loss function from <>, with the addition of `log` as discussed in the last chapter:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def binary_cross_entropy(inputs, targets):\n",
+ " inputs = inputs.sigmoid()\n",
+ " return torch.where(targets==1, 1-inputs, inputs).log().mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that because we have a one-hot encoded dependent variable, we can't directly use `nll_loss` or `softmax` (and therefore we can't use `cross_entropy`):\n",
+ "\n",
+ "- **softmax**, as we saw, requires that all predictions sum to one, and tends to push one activation to be much larger than the others (due to the use of `exp`); however, we may well have multiple objects that we're confident appear in an image, so restricting the maximum sum of activations to one is not a good idea. By the same reasoning, we may want the sum to be *less* than one, if we don't think *any* of the categories appear in an image.\n",
+ "- **nll_loss**, as we saw, returns the value of just one activation: the single activation corresponding with the single label for an item. This doesn't make sense when we have multiple labels.\n",
+ "\n",
+ "On the other hand, the `binary_cross_entropy` function, which is just `mnist_loss` along with `log`, provides just what we need, thanks to the magic of PyTorch's elementwise operations. Each activation will be compared to each target for each column, so we don't have to do anything to make this function work for multiple colums."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: One of the things I really like about working with libraries like PyTorch, with broadcasting and elementwise operations, is that quite frequently I find I can write code that works equally well for a single item, or a batch of items, without changes. `binary_cross_entropy` is a great example of this. By using these operations, we don't have to write loops ourselves, and can rely on PyTorch to do the looping we need as appropriate for the rank of the tensors we're working with."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "PyTorch already provides this function for us. In fact, it provides a number of versions, with rather confusing names!\n",
+ "\n",
+ "`F.binary_cross_entropy`, and it's module equivalent `nn.BCELoss`, calculate cross entropy on a one-hot encoded target, but do not include the initial `sigmoid`. Normally for one-hot encoded targets you'll want `F.binary_cross_entropy_with_logits` (or `nn.BCEWithLogitsLoss`), which do both sigmoid and binary cross entropy in a single function, as in our example above.\n",
+ "\n",
+ "The equivalent for single-label datasets (like MNIST or Pets), where the target is encoded as a single integer, is `F.nll_loss` or `nn.NLLLoss` for the version without the initial softmax, and `F.cross_entropy` or `nn.CrossEntropyLoss` for the version with the initial softmax.\n",
+ "\n",
+ "Since we have a one-hot encoded target, we will use `BCEWithLogitsLoss`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(1.0082, device='cuda:5', grad_fn=)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "loss_func = nn.BCEWithLogitsLoss()\n",
+ "loss = loss_func(activs, y)\n",
+ "loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We don't actually need to tell fastai to use this loss function (although we can if we want) since it will be automatically chosen for us. fastai knows that the `DataLoaders` have multiple category labels, so it will use `nn.BCEWithLogitsLoss` by default.\n",
+ "\n",
+ "One change compared to the last chapter is the metric we use: since we are in a multilabel problem, we can't use the accuracy function. Why is that? Well accuracy was comparing our outputs to our targets like so:\n",
+ "\n",
+ "```python\n",
+ "def accuracy(inp, targ, axis=-1):\n",
+ " \"Compute accuracy with `targ` when `pred` is bs * n_classes\"\n",
+ " pred = inp.argmax(dim=axis)\n",
+ " return (pred == targ).float().mean()\n",
+ "```\n",
+ "\n",
+ "The class predicted was the one with the highest activation (this is what `argmax` does). Here it doesn't work because we could have more than one prediction on a single image. After applying the sigmoid to our activations (to make them between 0 and 1), we need to decide which ones are 0s and which ones are 1s by picking a *threshold*. Each value above the threshold will be considered as a 1, and each value lower than the threshold will be considered a 0:\n",
+ "\n",
+ "```python\n",
+ "def accuracy_multi(inp, targ, thresh=0.5, sigmoid=True):\n",
+ " \"Compute accuracy when `inp` and `targ` are the same size.\"\n",
+ " if sigmoid: inp = inp.sigmoid()\n",
+ " return ((inp>thresh)==targ.bool()).float().mean()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we pass `accuracy_multi` directly as a metric, it will use the default value for `threshold`, which is 0.5. We might want to adjust that default and create a new version of `accuracy_multi` that has a different default. To help with this, there is a function in python called `partial`. It allows us to *bind* a function with some arguments or keyword arguments, making a new version of that function that, whenever it is called, always includes those arguments. For instance, here is a simple function taking two arguments:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('Hello Jeremy.', 'Ahoy! Jeremy.')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def say_hello(name, say_what=\"Hello\"): return f\"{say_what} {name}.\"\n",
+ "say_hello('Jeremy'),say_hello('Jeremy', 'Ahoy!')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can switch to a French version of that function by using `partial`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('Bonjour Jeremy.', 'Bonjour Sylvain.')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f = partial(say_hello, say_what=\"Bonjour\")\n",
+ "f(\"Jeremy\"),f(\"Sylvain\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now train our model. Let's try setting the accuracy threshold to 0.2 for our metric:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))\n",
+ "learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Picking a threshold is important. If you pick a threshold that's too low, you'll often be failing to select correctly labelled objects. We can see this by changing our metric, and then calling `validate`, which returns the validation loss and metrics:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(#2) [0.10436797887086868,0.93057781457901]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn.metrics = partial(accuracy_multi, thresh=0.1)\n",
+ "learn.validate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you pick a threshold that's too high, you'll often be selecting correctly labelled objects:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(#2) [0.10436797887086868,0.9416930675506592]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn.metrics = partial(accuracy_multi, thresh=0.99)\n",
+ "learn.validate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can find the best threshold by trying a few levels and seeing what works best. This is much faster if we just grab the predictions once:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "preds,targs = learn.get_preds()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and then we can call the metric directly. Note that by default `get_preds` applies the output activation function (sigmoid, in this case) for us, so we'll need to tell `accuracy_multi` to not apply it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "TensorMultiCategory(0.9554)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "accuracy_multi(preds, targs, thresh=0.9, sigmoid=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now use this approach to find the best threshold level:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy_multi | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.903610 | \n", + "0.659728 | \n", + "0.263068 | \n", + "00:07 | \n", + "
| 1 | \n", + "0.724266 | \n", + "0.346332 | \n", + "0.525458 | \n", + "00:07 | \n", + "
| 2 | \n", + "0.415597 | \n", + "0.125662 | \n", + "0.937590 | \n", + "00:07 | \n", + "
| 3 | \n", + "0.254987 | \n", + "0.116880 | \n", + "0.945418 | \n", + "00:07 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy_multi | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.123872 | \n", + "0.132634 | \n", + "0.940179 | \n", + "00:08 | \n", + "
| 1 | \n", + "0.112387 | \n", + "0.113758 | \n", + "0.949343 | \n", + "00:08 | \n", + "
| 2 | \n", + "0.092151 | \n", + "0.104368 | \n", + "0.951195 | \n", + "00:08 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "xs = torch.linspace(0.05,0.95,29)\n",
+ "accs = [accuracy_multi(preds, targs, thresh=i, sigmoid=False) for i in xs]\n",
+ "plt.plot(xs,accs);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we're using the validation set to pick a hyperparameter (the threshold), which is the purpose of the validation set. But sometimes students have expressed their concern that we might be *overfitting* to the validation set, since we're trying lots of values to see which is the best. However, as you see in the plot, changing the threshold in this case results in a smooth curve, so we're clearly not picking some inappropriate outlier. This is a good example of where you have to be careful of the difference between theory (don't try lots of hyperparameter values or you might overfit the validation set) versus practice (if the relationship is smooth, then it's fine to do this)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Regression"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's easy to think of deep learning models as being classified into domains, like *computer vision*, *NLP*, and so forth. And indeed, that's how fastai classifies its applications—largely because that's how most people are used to thinking of things.\n",
+ "\n",
+ "But really, that's hiding a more interesting and deeper perspective. A model is defined by its independent and dependent variables, along with its loss function. That means that there's really a far wider array of models than just the simple domain based split. Perhaps we have an independent variable that's an image, and a dependent that's text (e.g. generating a caption from an image); or perhaps we have an independent variable that's text, and dependent that's an image (e.g. generating an image from a caption—which is actually possible for deep learning to do!); or perhaps we've got images, texts, and tabular data as independent variables, and we're trying to predict product purchases; …the possibilities really are endless.\n",
+ "\n",
+ "To be able to move beyond fixed applications, to crafting your own novel solutions to novel problems, it helps to really understand the data blocks API (and maybe also the mid-tier API, which we'll see later in the book). As an example, let's consider the problem of *image regression*. This refers to learning from a dataset where the independent variable is an image, and the dependent variable is one or more floats. Often we see people treat image regression as a whole separate application—but as you'll see here we can treat it as just another CNN on top of the data block API.\n",
+ "\n",
+ "We're going to jump straight to a somewhat tricky variant of image regression, because we know you're ready for it! We're going to do a *key point* model. A *key point* refers to a specific location represented in an image—in this case, we'll be looking for the center of the person's face in each image. That means we'll actually be predicting *two* values for each image: the row and column of the face center. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Assemble the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will use the [Biwi Kinect Head Pose Dataset](https://data.vision.ee.ethz.ch/cvl/gfanelli/head_pose/head_forest.html#db) for this part. First thing first, let's begin by downloading the dataset as usual."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = untar_data(URLs.BIWI_HEAD_POSE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see what we've got!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#50) [Path('13.obj'),Path('07.obj'),Path('06.obj'),Path('13'),Path('10'),Path('02'),Path('11'),Path('01'),Path('20.obj'),Path('17')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are 24 directories numbered from 01 to 24 (they correspond to the different persons photographed) and a corresponding .obj file (we won't need them here). We'll take a look inside one of these directories:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#1000) [Path('01/frame_00281_pose.txt'),Path('01/frame_00078_pose.txt'),Path('01/frame_00349_rgb.jpg'),Path('01/frame_00304_pose.txt'),Path('01/frame_00207_pose.txt'),Path('01/frame_00116_rgb.jpg'),Path('01/frame_00084_rgb.jpg'),Path('01/frame_00070_rgb.jpg'),Path('01/frame_00125_pose.txt'),Path('01/frame_00324_rgb.jpg')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(path/'01').ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Inside the subdirectories, we have different frames, each of them come with an image (`\\_rgb.jpg`) and a pose file (`\\_pose.txt`). We can easily get all the image files recursively with `get_image_files`, then write a function that convert an image filename to its associated pose file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Path('13/frame_00349_pose.txt')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "img_files = get_image_files(path)\n",
+ "def img2pose(x): return Path(f'{str(x)[:-7]}pose.txt')\n",
+ "img2pose(img_files[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can have a look at our first image:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(480, 640)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im = PILImage.create(img_files[0])\n",
+ "im.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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yJ1sAI7IzWrA1/W/QxCMiNr4BFYYGAaq4s/5cfeURCECAKBJFVErSWUWjMWwcakYfhsjSCmwJjNGnGtYfZGhFlmtOm5RMkCAQVzGlFKBSBSToykIOEFApVndJki2GcEkd4IpVd8xgjAAp/R2Q2z/7VVLM380rV/2NGsYdnhc5ElghecwVeTer8NzqQb+jvX3sPXpU2a/afd/f9767F5dDdeyqVm8dKEZZAJuIlV0b+fj7ldV/GtYnUaz3wohHsaDj31L+O5D2iDNSyLlUh6uAw9EPrFerOvBhTZFZ7tGdxO/4eDhMxzy00QKo3FwizStvXKp8yDHEgqpBVbEk0pE9GI9v2S4RkMv7ZQWrV19ffC0BtRJ4FNSjqv9vSFa/7fJaPLCyA5WJCwS3FNF8R2Ew8uix58fD8qe8cRniQbcxSQ1C32HVwN8dTvZtwvX4ta3I3xEYQU3tU0JM3TH8Y6khKKktR/byHVdh/Xfzyrf9gKGw5+2gOJJOOIUQr35wMSWOkMrhLnTSUNUybnO6Vn+lwGXQ0AJxKMVAY4aWhKdQ/MvqZdagMo9J09FTcRKMqOCXHXWTViFMrRVgCCVwKSIYEsTeZgcABCCMGmQ04MAg1bGuewLJpWoGJZFqTErL2QqeZeYFUcOghN4Nof4VLXc/ekJZi4BuX4CXgNS7NilEZLCcarapDCZyZZdai7f0NVe5alVujw9qtvK7vsFCZ5U7GJmjvHm7OxqUy+0fWZ+KQ9JhMe6ULlaCz0pyaVofi5S9A1t8X6t4RzbgGxwC4qN6q+qZLBV2o1w5ds+DaDouYAfCr54BrL59fEG9HpXVCwBQWJZa86xH8X5HBQyUsL9jSDJevdOl+aYqjpF2AKuEvm3dUQjdfvvjV9x+PXHgiVLjZcUoJF0q2qLjhRar4XZvank+/nRr9XqdjiVZiog+wqqrD2sp39tpuO9jiVYQyo+dExVxeddcOSIs+q4kH4kS2HjbF1TTCre7Q3/iikUyl3zh7BR3AV041OTQnaqFuuXOV5McHDUV4U8lw91ICg2FlZYC2GUojR3xZ6n8PJzL8qaqRwl6+bQjThRWPAUcFacrv7LUK5CjSjXUGiothU9Uujs0VPfanYVlVxBWamaN76mIUkpadZEuShok11APrW4gIJig2BDj9QzF1yQhQkYniTzAzvWQA7VgQ4f4xHBTZa800mHZLTtb0GleYtBAKbChA3CnqpYQi6qPBBah6r+EAJQP5ZoVEFhhT+dYL1dtkKPBN/rA275C1FVL+w6y7LbF4WtH7+iYDTxy0JFVFMzx010F+NtJ3TsuHXJa32YNEPQxNOM2iPT2FcliP9PMU3JSQhAItVTFuYiU2rOSiSBWcwelJqFDRoGJI6rryOPVi1g1TJby1kc6+WqItxjYKy+ujEse8ZnKWbj9bkcevSP7Lq+Zw6F+GwPtjpZOOeZA1S6r6nm8vOUmlNeowO+8Oaqy6gcvv4soWBbdhYFHvak73k5lbAGA6A4zmrGUBpfMPxFQ3IuxpRCCTqqpCsRVpbjNJZdh1fxZde35Nj5lsYnHKxosHBmBjqUl5TIS6BjjLheG4pRBB9/O3MAd7LJjlGJR/IKmaWITY9uKBFjGYMBXU+jdWZS34yRvv0YFgaNqRZZ/Jpfg1DsaAstdGY2sbFYaIGSzlDqR1l1UxQVipqoaoMKS9+IuosWqFwBmw7kvbS4cg4A6bsvVr19FWwrxSAeyZ6c4spOeSQNX0OZy5+PVF0oWjiVLke3A/XBHKfNjQeQrEszlxTjNC0wvjtEwU8Qgfdskn73+6v53X3h1++S92ydOPPrQXbGZMxk8KcypI9I15tOTK2UdIu5VnPogfKpslVDbd0gwYQYpIK2kyEvINd48RkKZl6EkscKczoQa3tdxf+rOFNdMyikROqk0Wqx1HdUVtpzzcPhYssgDSS3sqCEEOAzuXnKHKgVLVwCy5H5DtcqKoXLm7QhceWpJoqLyV0i7Kl2Xr8JSZhwR6UcNqzuukndBp0BcqNAYm1s3Dp755rlnn/vO+fPnLl28fNjd6qxpJus//TN/4b0feM99d9+1vTGl57eJdR2V2++Cy1dlScHihgQSrnza920w33ENBDYrvUtyHhNcllIwUItudkcFBkqmUK2KcLcR2ScAt6WXt3QQS7+A4TX0wWgaKFm6bJgvCTY0tagifdydUVq7+2hMjtjbKJPH12Nlx4vbWa5H1Xcv7339y8/99md+78KFs6HpIL3Dk/eTZs05/cyv/wJ+88SZMw/81F/6iQ9/+PGdmYnUJjrjZ44mJ1fCG8OfgBr0OeJjVfOLGALGg00npUmIHL3mpfqXIyC2jMiMyOpbjpyM6FYbEFk2MxcZ0JzBiiQJB1VAZxhLMyAYa1yXMbuBnL5620sCjwoFkBXCgLABVh5vhiNLkqz7VLu8HPODl2uwpIfeMEuMGgMiRtCJGJrd3euHh4svf/Fzv/6Pfjkvuu0TG4fzg8WiE2ktwPJcrVssuowrr9x669bBtesHf+Gv/vgnan+VqoXubNx+v0sKwKIyePMybFjVTcdXhXSPm1d3XNGy5ZQti0XLOY855IPfCRFCjSQYWBxW1vJvLiMbNK/HZym6pewEBqKuCh8s2aB0q2Awqmd3wgZUsthNgw8zVImN0tzd3Y7urTqdsKIDMSrhwTguHxRD8/Wvfes3f+OfqEz291+K0TZPbO8tDvYODkgBFmJp7jx09skyJPrhKzduXLp27fTdd3/6w48jHzg9MlKcYsGXVFiVzwSkxnGlwmH1hgWoWSJFmqgYQwspqUixVC1LBWhLTl21RKs8LcFgEdhoUXOEBwCUyHpRZNHEk0myXrPnbCLm7iGEAb7wUjBQ5Y9wrBteFQ5Fzg/PAMus/CNGv9QoW3mmqtoi1sbwhlfBC66IO3fHbVDlgFEf6SBEd3crFtwdJAHZNs3T3/zG3/1P/k5a9BJElPN5c+PWjfl8L6V+Op1OJu18ngShT9lNY4CwE1tce/N7f+f/+Hf+2r/2c3/jp3/89NR6ZsKFQjaDibvCY0dFkYjwqFO3+hikhtHtXrW9WRn6XVjjPBJSJAchE3dfu7W/qZOT0pulbCJ5jDFgmZ7JklHLo/7PeGDN3I3OEgEd85P9mKuwqll41G4aXdvq8AzxzvFl4IpRsLJ34MoRWTYk06ME5iCrkbvD3/7Mb8TIZr2h5/3D7urVg+mGDvWGKG3VSJnPF5N22jRtE6N0h9Mg4eDN3/ylnz/32tn/zn/733jkzJb4gXgmQ9E3K/QiSs3Fqh8x7NXy/gcHcbkhJOlSaxhrPSFQw4mDEsSKVl4eBawko427TTB+6Ve/evXZ5kd/7smdh7bcDk0rrUJQBC0iQoYbKEHpkXHH4LG7m3F0OCHLPNbBcVq1EmUk9zHqVovalqSt+8ARazrKqwMEzWoHLLl59fXjJrdN+09/+zMvPP98DzXXmW4Y9mPb72yfTjO5eWNfNcbQNG2aH85jRIgioUnM8w4ivrauJyb51W9/4Rfm+z/3c3/9I++7H9770Lhx+K6BkEVT+yDB38GuriaPjGeEPuQIYYBflmRbcv9RcnLUgwMHQ4B46cab8u2v3rz01kd/8gfuf2qr2WpMD+BB6OLFvAoaXEDLQIwjmuekDmIhmwWzcTcJqo5w5u1uA1EB53puXdxAI7PRjfSKLi2ptbw1Dm4wS4ae2yixSQdZgeFV1hVX1xxj/N4z3/mt3/jH3SJfuX59++RprIWutxBDDC2naevEVhvWDw96N1WNMepsOvHMg8N990xB35tg3kSef/67//R31h5+9H94itOsySu8RJZcuHq/MnSULGb0WNnNaqUFKUh6RmAAqe7K2h6GxT8lUTPbiz6unnEiCLZHSRsE4/4IADpIjZf2LofU7B/sXb711iPPPfLoxx5/5CN3ISSEaNRIi24uIhIAF7Mx2Xj0goZwcgXcRUSUpSL3jgetPLDlkau21pIvj2c7H5e0YxvHoTkeSbo5nWajquMoUhwmQD9f/PIv/XKX5qHRra3t2XS26PZTmk8mjSiZmxBDSt18cXOxmJe02ZQTPMSongHQbb5/YCEsmtC/8M1vf+Ubz/7kD388Lm4Z7mjs/gnLB+VSKyqqdVIpNEgFh5e2UsVULD25FCTEeSQuXkTl8aSJeOvgWux9s73V4aal+Vef+erf/F//a+954GRn0Scb5kHFil0MEZgJObZMwIBmlLV0yFaE1aofvErjwWGtUFTtUjnQeHzZql7h4PjWiE+Rfiu6dgQ9jr8RLuCtG7euXbyqbStmaxvTlOaWcyHkfD5Pxpy6frEwS00bZmuTGOPhwbzLGTDQQGRQpZlM2r4/6OdXf/2Xf+Xhhx/84N2blhccq6RGeAtwz+PuH/PMB4LUfZDa4sNZPMzhhoq5OuwXBsVakIjRIx+FOQepSBIlOT8ydR323Lq57e+lG7v9zQuvPHaPPmBxi1v36ubJDAlCwAS1N0VZxWxW1bFbkQwdv3RI+ztG4FU7BAPuUfpZDet4ttCSm8HRuPCViOHqy0i6m9OHMzDuqLWxeem5F27sXvd16bNlmwup0q7NtiBIybrFrfl8TvOgIQhT380PD7JZbNfX19YOD/bn+/MmzGIzEwhkHmeLm+ff+Mxv/Najf/O/u+zlUGG4kreMlfoDjK0beZTD6t4MXYJLp49SOVuy2wmvYdNRH7P0evXBEcfR3Vja507GpF3ijLyR83z35o2Tp3Hxzef/+Ppr29t3bd91/92PPNnu3AsnaOJA4NhFUETGbEistkmglB7LJaqhQ8cwEVkGO4eLKm+koajSETY9ctqXG1csF5BS4A4f5TTotXyo7O+R1EFCvU9f/PwXsu53Hfs+gxpDo0KAKc3n84NgaKJqEzbato2w5Hs5uVJDb95OJtsik2m7rgFdd6DgwfU9qJx7/qWb8/mJidCGbS3AgBRoaBnU9dL1TIxwUkBVD8FDMqdr600xGAVhCJ169aGH5KHhBBjogNCKVK19rY+CLYbiK8Pifj5AmKxpa4Zm0j388F1Xr52/fmV9Mr1+6sq1g8P5w098eOP0vSbCwIAw9OpZGlCFSCUjc+TagnRIbZUyHIgV0T3KVVRHy8yWPWRHuq4QeEn1I47v8qDY+CxWnicgIvu3dl9++XltJ8GSMIsI4Dl3XdeRCLrWbK1P2K0Fv+/UqaZtDvb2r+/t7/dpkTtLfd8ZwSSdGNu2bdu4v7d3cHj92uWLB/vzE9P11TIF1N09yqnDNhTcA1KLUaXaLCu3csx2GWq8Vz6QA8mPyP2Vj6gkAiT+9E/90IXXzp4/d4VY3HvvmT4tLl9Kk6Drm0ncu0V36+b+Uz/w47NTO9nZElRZTdwpn1Soy8GDKvWvpd1X6egmtbbgGMpaub9Qd9TEqzp4SWkcORDjGl9f4cmBwBh1nYiq7F45f+3aJV3fFoaoMaU+u/XdIiVv4lQD7PBwqvMz953+9Mc+bDnt3bp+7vylKze7W4vF9b3rcy6ysQjSxYIxxnYySf3i4Oa1m9cPHjy9sbzaok1X8gIHEghdCC2Ac+lfXdrgVkV7VEmv2EpHhPrgeRxnABwxe2VIspT4Yz/1M6d32pvXbh50bz79la9+75mLkqc7O5myFsQXyS/uPjfZuu/JT38qO0UYBQzLNiIywLxmhqF5ab3b8lcZA0q1t6KsBM/vSLAx//7IUcBReh/fvnJcBqhrYOOBmdB3+323L+3GLATLllPq+0XfOyBmCzi3p3LXifYTH3ziEx9+sj/Y2791bWvSfO3pl/eMUTiZRMnou77Yy5Y5aXXayjzny1euyfvuGy9pqXSPEKXyUwUnB0ot+VownFHgnfOfKmseuWsOoMcgsRWQghHEg07uXnvPhnZvfOvm1bewPtONGbc2Gojd2LsO7GvcnK6vp8OOTUiNMntEyd2BLAENMXpxA0uQaUS5UGmNMQHo+AWvELvAjEOSKle9jxUwdqVK5QiNS44R6KXbeIUOhDSYuQrMUpfZ9Cn3nZHqkphE3NcavW9n9kOf+uSnP/XDm7MNk7YJzQMpX7x8Yff53YmroZUAyoJijWJtOonKg0WQKK+9/gp/5KmVBmYlrCvHzjFZb58AEQg3wsqUCCXEBVYIXVp3VRyL6k6RoUSKAjYD+9aPH0JMy/547oml4aN4tH7+1usv/vHnPrv71psiPHnyxHQaPaObH3qMj7z3sQ997Ad3Tp/ePzhs16a130S1oBQDoIHSC7b2BISNsNewRsDjdgKPBvSSiQcleoTAsrynVXYv7tIxknMIuKB2ew03dw89RW8wT4cp9Tn1AAgHqW733X3XX/zzP/j+x98fTdJ+FolNM9ve3n74oft2ry7eunJtar7fddIYgEnbntzZnIi/8VZ3Y95fv3bdvRg+R2/wDvc6nukjzqpISdcVKb7tuDE1prCC6Y8228rBXioxjHsyfIsz/vHnPyvdHN3hWivZLRv39tNi0W+dPvmDn/7zDzz6fob2MOVGFH0OoVGRlVJELLV9UHEfkFIU92nVU2L1H47d63E/eBXoWN2G0XI4RuPBT6g47XG57SVzQ+Z7HTNy16d8kHMujc3gFOYTm5Mf/oEPv++xx9R5cGtPsQhty2AizebGzsNn7l6bNCZy9caNS1cP5l2vHmJu77mnuXHj4PKevfeJJ0IUSz5Y+NU1uD1ndgAJliJ2tIdWfchBPfkQR6v9vwe7yYsHDKkx9eGWR9vFByMYAOLFN8+d2phI5rxL5geQsH3i9Ic++dFHn/rI+vrdXZoENhokEFZM5aEgY5A61SsXMw2BjCI6tHldCvBBDR8nMKvoxeAFH3WTWHPNK11HiOAImavTXOd5LBH5QZ4TgWhbIdD3h+a9uWvJQgE2p5MPffCJRx48M58nhXuag5AYIOJwxfT0Xadn67PEfNdd22dOHt7cO9DYbm9v3//A7NLlvXBl/65TJ0E/BiGJiC6N4du18vg8WBKgSt7EWABQ4cw76WOpJ+lOn3aHb4mHt/Ztvh8U21s7Dz30wQcfefyu+98z3dhyD33yEKzmGTBQmGHioRQJKSmlw6KAFEmBCF/44neyHvzopz/ehFA6oEOXeUZHwUstVe7u7jSiVtKuohzDca4I0fLxce07ehHFTtW+TwyhDXSYSaO0zdPai9OzREpScuEMRFxf2zpzz71wW/Q9HNYlMUfQocBe2+l6aNtsyS2fWN9ZeI5tO21aDWlvf//xDz341FNnckpHxEaBmVygAXDEKNCcetKWVF26TQGEiDmz0VXDcL5jHfyAwsOlIsYHEEVJsoDS1UkOQyTXa6dxAuJx68TO+z/4vkcee/zU6fuTi4boRN9RA0qE8Oi5IIcqU7AOwhLB4eHipRe+427f+tozB4fXH3vgnvvvvw+0qBV/uROBMfSMOg5drQIdK4+PxTuP5Mesktyzf/Or39TZ+sc/9pQo3BeuGrnx+F3NzYNw9TAvKC5JRcRlEnXaKC333sMRIOaePQeNKI3DJalQEVRjbIU5gaLSmKux+Zmf+cubG2vpsBvF1fKyVQyu0JdfOHfzZvrAk4+1TXTvj4kxp0OLyeoq6rwjry9vcyXZugIgqyQa1dbAVBJ/4mf+ypn7z1DaZM1hv2giY1gGU4tXW0TskL9cayONLlYLrWLUJz54Xz+fn339cHKLk2nbpz6q2EoHglVgZEngo7aRu4djft/y1yNcOz4ef5qZ069dvXT+7Gsvv/B0CrMTJzcffOh+mmXgrhN3/9CDj7345pupu2EpWJwIMFF/5L5TJ7Y2AkMQJSgqcdoGIMSGhJkLxd1hyQiNYcKa534wX5y+775P/uAP5i4XUbpySWNpY37ztZe+9aWvzvNs//Dqzs7mex97j2oJjxRTiy7Z0SAEuPidb/w4mcfHgrDyXfVPqjqkxykosfNmkRCFcA++UEYyuqve1jOrtqbCEDWiqFBcQJgbZC3E6cc/9WPw9Rgmli3EQGHpn3knAleDsI6gWwYbliHClXQfDMbUcT94iXVD6NjcnD348Okrl9YOuTaZTbouN9JA0XXX4+7sh5/8SH7524fnbuUmkPz4U+/9gY9+YKOZBMRZO2naJruT0KAxNkGboArLXbdYNIckXVSM2b1jZp/++r/xs1tb21wsbt96lhCupXRw5frl7+S4ceGCP/zwD5tnQIe8sgI9u6iAkv+kClyS7pRBk7Hc8m1uMYAh3ARAImA0eCAkFwAtDFkjg6MBgQtNqHBS3UmBiNechXLuQkNn/oPf+0rA2l//N//KZBq9OrJlfE65FFtSWpaB+lL1a+WBV1sDwNGikOMEXqV0MV9jyXiebj/+1J9bLHIgPHdsm0h1Tk49cPL6zVuTg+bMieb8wUFspu9/70Mbk6lkTtdns/XNEKNazpZzn3KCIE+ESvfMJq6pamaXI5Ro2Kw1d9/32BMxOSU7AigihhpTEgAqmp0H/Ykf+fGf7Xx+4wYnk0mf00RjcZTJ0rE+NiKHt+Zvnb9x75k1MytpcXdk39HD4hHkq+TeLH1FKVOaBIRHVOOtaF8OLijds8jg6mitXiGg9Bhj9YTr4BwKVLN434svbu7tdt18bTYBPWjJKxgtytUzfoegb4k6rtDvmBm1VByrcCYAOJyeQ541U8z1M7/6WyfveeAv/cxfVIHDekpc21zb3Hzp1Vc2Gn7qQ/df+P231jam2+ubcOkdaZF3D2/durW3e+O6u6tKbGdNiA08AO2kmUwaDejzoQG9S8phdureySyA+/TA4lKDY3JfUZaqpLYXr+b9jo+/5z0xxqG0zZdukUOCWrY+zcnpklMrtlV26R2xrdWDPnxo8XDojGOyHIhSOVg/VEWR3dVdzEpZtFrOBTIMZRiBF0grOJmYofjzf+HHPKNt267rJm0sPp8u/aXVjJYjsKUPaXtcsZ6O3cAdib3yDKGgBAHXpiqSyQwEumSYTtd0Mt3aObGtdz3w6OnJH77x6MM7O+tr1qe9vVtn3zp7/catm7duHhweJsspZZewvbl1187mye2djY21pgmH88Pd3YPre9dvHcy7Pj7x0R/4xE/8t2DubGo5vRy5PBHRwEW3/81vfOPWHI+/98l2MoElFoiOBnHR4uCqe9aYCiSg2sgQUFnBA+/E0IPqXQE9VgkNERxvRupDm0bSyNq0AKUJfik7E7hKbBqndpCcbLE4KOU8lnO7dsqz39rbmzSNW9s0IYRGQ5mJpkWLDa0WOZ7WlYCBl2YSqxGn2wm8+utIbBKBDTNm69O/8W/+lZ4hNNmdKlHJ2Lbrp08/ud6+9dalfr724L07Tz74wUloMLGZQhaH1t+atJxMNrL5weLw6vUbBwdpY+r9mvY99/fTubMX9g4tTLm20Z7ePPkX/9qPxKjojbogVW5r5U7AySDpo089cvVGL7FBmUfgpfq5ZBh5QXUX3cHaWhkn0swPuz7n9fWZDI4ias9j1E7Bd1pHCLt8BpF10h5UBZrJ4FDAAyKoJe/KPUFoJiVXMEzjl/74m1/60jP93A4P9rOlyWQyW9uezHQ23f6Zv/zpjTXCGoDG2DSMDKISSqfzIWqMAq1KTc+HGJFZczaKGpM7qyFgmbZXEJ06Ss5KZNxcJ+t3h5xSzsZeLImEaePTB05c+fYVMqnIj/zQD55sd5Bj02yfPimW9tZ3u3Pnd6/sHu4tcJD3gnBj2q61zeb6+t0n11577ezejd1Di4t+epfKEx94+L7774eZiauXpqhFPZbLzsWGgsWNjbvOPPDw9f1uEg3iRIvQa6aZ0ijMyOatHe7vdjns7Jw4uHrw1a89Pdf80Y9+5MT2DLmzZNn7xVwn7ebGRuuj+CUH7Tv2zxpt+JJiDQCxJouU3PWaRzceQQI0M2pBzrKIHvTym7/1O7/3e78rsjB2KiE2kyY2DJZSur67mExv/dW/+tPJTHNf/St6ybqVsaWULKcyea3PLwOcnRSHSwXrl8f1GE+PwcExgbySuTI03KXgoDCq4CBy66H7bzx3djI7kSy005lHdKmHTCbTjXvufTDONiBrkKuTw8VJ2dmYzrZnG+uztYfvu/eJxx7s5of7+4tFYm7WHj5z+p6TW25exrIcqwKuGYPDIdzc3Lh549ra+l0mlSDwkgyKUnTvjsC4sXZiceMwAlcvvX7u9ac3T2+ff316UZgX8xu715IvFn3z6Hs/9MGnPujZl37FsAkr9MIxQRKd5jR4CWQMuo0AimGbFcEtgCRziOHXfu13fu+ffXZzPZC3tG1ibEIgxCwtlHFzPXzus1/80Ec+/r73PQgaHSnlUXqszlTgYEQUArtbcYshUhh44M47uIODITMKojFVRAoGXr9FRV2hVEimNts78czJ3YtvxaaN00DPC7hT3FuR2doUd52UtpnN+wXJJjStTrY2th4+c+/22vb99zww79whcbJ+anM2W1svFzJEcpbGKapcBQmDb23Nru8uzr76+tY99546PYN7GUknomQp2stAbNtm0rpn3vfA3R/44EN7+3ub07C+viXAfffdG0JoJhuT9Z2cstRpZLd7E7ZqmY7eY503U6rKSDg9SGBxtMSlTEzy0jOE+/v73/3m020zNxqJrdl60zY599nmuYMqY1zs7/e/8Zv/+N/+X/6PJ1GUmcu8fAmh8Jyoag2U1IPnzsK+bra89EEQHyMt6ojKFTu8ACZjmjQAVVGNhAIUhNYByQ98+iN3ve+Bs6+9dP7sWWVA7ruFHSK65ZR6MGxMtzZmW9QMSBObkzvbgXGxn9ow294+kV1EIxgQZiE07n5bvzhZSYIXQDQgp8Uf/v4f/fhf+7kySm+YSkgtLaYlA+bUEMQocX3jB37oxw729vcOupTLJNMAmURtaS4w6PGGeFxZw3bJ+Hwkx5S5YnCVfIPohFCcgLjCgwdoc9gt5t01kZzdQohADGGSk1vKKS9CnAfRJvLl5779pc9/7Sd+8sdSmoONgy0ojEKXQNEA0KWMNIC7iyuzlBwHKkb7i5KHLve1RfogqJexuaGUqUS36KV/a5lPAxkmbTkETpXp1uaD2xu7V9m/kBp4Ms+dMMIBs7aZrM3Wmhad9TevHwTz7fWNtdlmoEVljHEWp31O3TyfPH0ytpqTDbObB/KWhtW1WsCJJsfDNshHnnp0OmV2tKSjBSjiqqraiEaFU4mgCAA3PO2F2cmtpu8XXSknoJZe7Rysk4JhHTGgpXrG9cgVZw/CWFJl3NTF3VmHEA+RryIAzQmRSQhPf/vbN2/sztamRiGk6zqSOfcppRrDggIpSP5n//y32/WNDz/1kc31kHMvoDadeBzsAlCoJTWuZs5VtaQDJiol8QUiCMccJw5Oc2GF5TleSQoo6+iBAB3ZMZlM3ehRjEYyiMa2mTbTtpk2TXD016/cunL5yoeefHJ9fXNzYwvdfHtt/cK1q3EanMiz9v5HHhiD0MdYeNw0FttawubWzpkzfvmNW488dgbSC9fH7IuC+Kio0XMueNPiS1/8fJid+fjHPri2PnGWli4KKkSkjK4c4f2ltHjbpSVTFSVprUTr6MvOJu4l99gE1/f3Pv+5z4VYACpRjSS7bu6ei7Y2Ax0hqLK5sXvr5/+LX/y//1//3te//r1sE/PQ5c4sD5851Kosv6Uy5bG/opQerpaEHLmlJVRZ6Lt6osdVn0GBBi00DTSmDAlRokIdYgZLnuZpsbe/d3N3twmyc+Jk9nZhmYrpbNbE2OUum28/fP/69sZQq3OMrqWVTm21r7QgjchkMt147ptnFwfRRYG5VBtnOLV1aBnoc7f9vf0L+wdvJc/QICHWqa9awabV+x1V/rFjvfp8rKR042p1V+XlMZkbsQ2f+4PPvvHGGxtrrZlBQqjz4ipmTpIOdwlB45oastnNV5//8t996bkf+dEf/df/9b98191rOWdIacvDmmpRN/1Inuzo1K9C17eRFlhxC45AmvWVoyJcLoEQHpoYYtvNbToVDQE1mFAKNr3v56dObR4e9n/0+S8Ba6fvXT+9ORNwnpPlPoT1M48/WjNoMO51OYsynrky8koApZrr1sn1+9+zt+gvbU1Pgmm4O2WNHKuIS7HAufEjP/QT3gYNTqggUuL4lqP3iGN3LXIHxzK6gQajh2iElZp/CSSju4QQSIdg78be17781aZpFI17F6ME0phDCBxC3cXVUW1Co9NmmlMnlm8dXP3Dz/7m2Tdf/J/+z/+txx8+kWlTasNMRjpKIy4s7axqIdStcgyzD44EuUtSefGMjmF5KzFEH3+OZomQhIRZM53Ew/1FSk1UgRMqJjR0cAvNpImt6EG+df3aW29cuBLW1rfO3H/m9N07vthv79o6ffo0MsUxzEIphtOql0J3IyJFlbln026u/5Wf/elsQhokAj1AYMhwBgQMTgNEuXX3/UY3k1JkiaJxB3do6FxY4PojpbwriiwMDED1lWTV1cSoJTOTouHll1++dvVqjGqeCIRYhtGNRUIsu2xmJR5DqGozm61PQprFdPblZ//e3/1PXzy3C1P0fbZQKqx8KM7BkufuvI7JH/AIT7/d49uWCBFU1meToNKlvst9cu8tH3b7B4uDw0V3+cr1586dned016mTZ+4+tdU22xuz3vPlGzcTmnsffER1Ag8idx6oW7Zj3F+B9L0I1pp2c9rOYiz9a7TKmHIyRE+dOr29tWXmpGdjndR5J7/29m+5/Y+rv8QjKRSAO0OoCKJKrVdww7ee/nbOqZ3AYDFMQpxAlGIArMxzHbbdzCWZQoJK07aTSZMODteatQvnXvov/vO/92//L/6th++/i2ZhGEPntuyzQdLdZMjJlJXGF6spA6MUX5o5w7wVvK3fDAB0ahCaxeAx+P6iD9QoIkKQbunm9Zu7V26R2l3c8/0uL7JO2j0esstJ8eCjD991/wOlMAErfStv/6JqPgEA+r5HLUSIInDPglCKD0SMdJWwubGdDlM6RAk0DfDc0OTktt7U5UuO3egAC2KAgEQgWoKxfnSxdruo1s3e3sEbZ8/FWMb7WDuZUsI4QH71xSRJSylfv3792tVru7s3ssOh2a0NcumV7/4//9//5YsXrhpzMe5IOl2GWW1c+r5H+fXIyWUtFV193ikiGo4DwgPk6TI2b3Nan2II00mTzA67xWG3OJzP5113a/9g0S82T6ydvmc2mVmz3sjaVNqwvhnuuXsynfZ3P3RmfWfLkUSG5BssLYbblpBwuPkcsgCWg5ZLb9dBqhNQMy8jT0t34XcIH91xZ7g0XY+/Ut2ze6Ybc+l84GR2sjZ1omjQZ575zu6VS22EmzXaCjyztwpFlST9mhYNOJDhh6nb77qDw8O9rrMQWs+Eowl4/Xvf/u1f+ZXDroASQ4I/CQsqorCaQ1g95GX6zjH5DJKeQRNiaHjhAhN4GTKpDnVXN6VNWs29vnllNwZy761pn5uwNp1Mphq7rl/k1Of+oLux6DsJUQJDaONsmkI+YOdtlCYczhezjZM2OfX6m/PQrrtQYEDvyKzhbgEUUGF1BLUAvera94QrvDTSlCXSJBBVDaKsEB8AlnxuaulhRGBZsYHyFWXok4gSpS0Rq/kIlJ7SQ40WANFK+aX29fqjpGTCcu6ff/a7xdRTCU0zq7kCABDBQApdnUovwYlSXCpDypWpiKqYpdQvnnzi4R/9wY8u9m9azuMXDrx2O/+VM7Q0Du5woJHIJKLwoNYGRiUCMoKEdhLayaL3r33l9f/z/+kffu+5q4898RDSrQb9bBpj02xNY0h9N1/0rpaDE6EJ7Wyyubl5zz2nH3r4vkcff+Dhx87snN5GDA+955HXvnfh7/zH/9Xn//iVfZlg0obQBDTRQjQNhLqAcemZyQA+UMoQrmOrzEstMpJ1YOISt3tHS+L2dWx6+HLF4YMqjw+5BGXopYmE69f3Lpw/30waBybNNDYzAs5M9zoVk5U4UstYytxaddchc8NiDH2fPvaxj/25P/dDOztT5s5tbZUpyVqlRi8RsbIxR1Lnb79z0iFGF8AIQ4A2jbDtk16/sTh39s1nv3vu7OsXr187f/nKwSd/6i9Ntu5J1CA+XWsnbbO5Pjm1vfnmpWvz3tsYc+5DEyfr2xs7O2tR5vuHuelDgybONpt2fWvrud//3OW3Fv/5f/bz73v/+z754afe996H7rt/ezalOOmd0wRRUDPaBFJiNyr1ycK1XDpUtRXQ0A5leZusiOyqjuc7y+0hmuSrxZXgCoFH9h3elUlRmZx7/eLBwV67NgG0iROnEs6CdCERDpR+fBVhcboAxYBq2sZpOeUgKqKuTbOxPlmfNdMpZCjqhQuUHCr1a9SDw+XK25hOI7sHDRJDyKZ7czt3/urLL154+aU333jzrb39PfekSpXFZHPxD3/lN378xz+588D7r772HY06m7Tz6XRrffP0ZvfmlSu9SGjk8Ea/6MJ84UUUJHd03s3z6TMPvvTy+ZdeuRinbeThq985OPvSy+1k7cwD9z/x/kef+uBjZ87MttenEWre1y6HlBHhP96Q9jZBNdZsHqXZ6q+Dafm2VB5w/ZqhPQQbFBCnoE75G5UdCEPIwKuvvFQmAsQQqLEQEQSkKcMRlEInaMqAIOZuUNEQQ4xtGwL6PnVdJ1G+8+3n/vxP/uRkfTs2tVsegGJPFg6mC5BXAQRAgChSBH5BNjwIwQjX2LKHXL3an33tjRdeeuHNszdv3LxxsH9TlIS7Jy+dt+DR/caVV/5v/9kv/jv/s5+bnr5l3X6j19ZUrJlubW7fZenmrRuLw2QWbtzc3b14HU3TTjQiWRdO3HV6urX5O7/1hUSZaWD2GFOjh3lx+PoLV994+bnP/f7OqdM7D7/nvvc/8fgTj921vdNqyEAQte7aG08/87VPnD61s7mtcKlOeRYXqb6QqMTqLa6MoFj1a8Wh1fYY4T3lkH9Xy1DpQgCBVCKDVIlCj06vRX+1S0j1kcQgAXt7N1977aXQBAGbGFTVkIhetCiPUDr8DGbRCmOJDHmZOputAZL65GTX9WZGrwnvpUKi0LPq2SWMtQQ2OFRuFZ/BaRo8xPVXXrn0hS8//eKLbyzmvXmnIil3zkO6pWQ55xCiqNIt0Hdm+Prnf+eXz9z9F37sA9v3PLGW/ebBK9pgfbqW106Ih8O46HLOls2T0wObSZxO7p488dRT33zuu69dON9ON8wXqoHQ1HfFMI9RqG/duHFr9+tXvvHH3zt1auOxx9/z1Ice+sAH2itvvDq/cWHvYPdg7ya4zeON5OuNlllr34/GXeHaQVmNtCNHCFAAiQYzZkhUFPfIlWW2NVTktddeuX7jSjNrQ4gkzUuNeqngDyoq0Dykv9X/D8QuSj0lTCbT6XTmDmbv+67vU554MFc190C4cDnQd1Q/400c8wdINk2zf5g/+wd/+I1vPb+3d4s1BO6E9+kw514UKScziiIglDGQMXBjkn/tv/4v1zf+J5/68PtO3r/5yM495199wS5emDkcGzHEzrrsRgfEXMwbfeTJD7xxce/3//CrGieQRAZScnbQRCw2wQ1chKxdjBbbsLe/941vXHvmmWfvf+j0pz720FOPfeix9x1GUcum6uUeQI5jYQcTpIrfETtavd+37SxO5dixZewxAJS02eJeRIIc2uEXPejiQiVA53PPPQsMXdBI9zxaaaqsYOrx0zdczcCKlrNnmbaTDv1icZj61Hc5KEJoVrt0lq/HmGC2YousHtK2bS9f3v2H/+APXjt3CbEnU8pZpRXRxeKw7+YFt8gpEyBVJQTRRGR6pk1y98u/+A8f+9/9b7cf3Ors0fd89IFTV149++Kz/StvrKOZoUmGruvnqZ9u3/3gBz705pWLv/qZ3xWZthqoZTS7OgXwEKT0gAsIbp5yH2NQCaKLbPNzZ+eXd3ffuLB473seUqdZvuO4sRJGkJqf+i+w7vC2cuJFAEqEowJjXpz2ksgBxXT/5sH5sxdCbEVEtUT6MhDBaJ5zXmgrMbTOXuBEBEKZHFBy9FnyvNxTl0Ft28l0spY6LhY3+0aDbogsRFpVMiQiupNuS6y+mlYu4iIKCmGTyfTNc5d/4Zf+6bXdPfcuHczJlNyns8hk88U85z7ECIO7kG7mWcw11hxWSAzx8Mbl/8f/5T/59/4P//tH73tg/+pba3e977GTj9x95tk3n3t6b3fPw/qJrW3d2ZmdOPO97774m7/26znlSTsBRBgL5w0eoNIDRAEXUcsMGnJBhzTFwHnqf/+L3zhMH/nYY6cSU8tZcHjlXSMpCIB4cLHiDGRQFIG1406BVmHVeRJC3Jc2ioA6wE1FOKDkZqO2SnXxWGErPe51icjzz79w/fqNjc0NDUVJwMzJVFKoRGBmQZhyroJfRFWZa7k3AHcPKkGCaig9nm9cv2XZcy5dG0rSLlTEpbYV5QBlFINkSLNyUNp248Xn3/j7f//X9hYZgsXiwD2JlG5RltIi5Z4kcuEVKS5fyimGgmW6myPb5rS9fvGtv/3v/a3/4D/8j37shz7aH14+uDXfnK3f9cDju1cuoZn1Ehbu3/zaN//Zb/2mZWuaViRySD0Y1EfJXCdpZe6GO7uuKwnPIYTkWWNaQ/f0V75y/erj//0n3s+48NzRJ4DUaEEVMroqhJcNdL4fb/iYfzU+X6pklyHYAZmmub/44gshhBhikKAaq7lvBssKxqBSmilVw65ip04HAoubwEyXGJsQVMT6fv7KK68fHqaUzCpYKWTZex9l8nBL1aQvzzQxvP76W/+fX/z1mweZ8K477Pt5zqmQLqX5fL6fU6q90axMANIYY4xRihdGlM4ZDl+bhFsX3/xb/+Hf+t0/+PrOqUebyXaHSTzxwMlHPxS372nXdp7+xnd/5R/82mKe2smaxgklUGIITQGpRGoHjLJbY8Bm6DXiNR3AnP1C3F5+9c3PffE7LutedCJ9lK0yBsuwAtOuytt3oOqKqh5Pw+jzlrdq+SZZ6Tfg7oDs7e1duXJlNpvGJobYhBBUg0BEi1oLWnIRVEIo86YhgjLiqoZ6zAQ0s5wNIiGE2IRLFy+dff186nOqs3xqM/EikFZO2HK5m6rcuLH/D37pV27t7bt4tmSW+9Sn3APibnt7N+fzfbgLQXfLVaqRiGGI/BAChYopXWxjpnZ44T/43/ztf/8/+n/dSpNHH35obTqZNpPNycaXfvdz/+i/+v/mzqVdM6o5HCoVJS5IYpFWQTW6l5Q2DECCszQNyhmGeY5dtphv/d7vfe4rX31NwymIFcdmpKYM3ePemUdJ8mhmy7G/3/EwKMooeA4fMXRdu3L50nyx106CqsRGRCniGigK1UYQSaiiadG0AhEym3dkFqG4KykGmFBSZkqZkGkTJzHYc88+t98tkvXmRhrNwEA6YPBhnCANdGGpgMqK+Ku/8vvnzl9FFJOUrO9z72aWWWZNeJ9W6p6qKIkhTJqJICqmKq1AVBFDaGovKIvKSbz5a7/y8//x3/5Pr+2373nve06fkH/8j379l//+r6pq2waIBomKECBBAMlSGiqzNtUV4TCZpKrIomEJp3iGuffWd54MB7d+7TO/88VvvYiwVnA3MghMCPFGaBhanVVyOXxILxMGpQpdYGWMogzpGSuM6wBFSueeZUg9cugyTtYcdDcy8s03z7dt2zRNDC0h9Cwq6m6seLhKIyIhaKosK+4qUA2Rnt28sAxIOLP3dItNUG0uXb54/vxbO1ubXkZYAUV8jtcKrORbQtq4/sy3Xv/yV74xW19zd1Hk1OfUWU4iSlbxKKIqEYildiI0ITZNsQVS6s2MNNEh11oCRNU9Iu/M9Cuf/+1/93915W/+j/4Hn/mvf/7zf/DZ6fq6MSW3MJSvVwU0NForaSnDhKE6I3MVmig3EoIEFZLZ3SXnvZu/9Zv//NSZ/957T8+k33NXozqPqMw7r9qyESvm1W3cypJLVaoIln/XsWcTYaWYkZSUFhcuXGyaqUgQCTmXAJ0TJhBn7+yPuqdDr5CijAvKKF5nj7gJTJhz7rLNF4uDl156rev6ru8t24odceQ+yZIXYWb+e7/3+Xk3X/Rzy32/mOfUlTmjCqaUUkrutdaxXIGqhhg1huLZUzLUQtSCv3gx3lTNHY5A297Ql55/+t/9d/79z3/hG+36hqs4okg54ZPJZNo0kxCaYvSGEFRXkvjHJvlHZyuVkXJmGUDphx/Z3dq9+uuf+d29fooQVQqiWVTkiCIsmbLcDYe8AJEiOY4raYxAZg1wHAHttaKXUoZsFPLrYjE/PDwIIYQQnU7mECGagSxqREcszPuu68x8cBsMYqK1FcHQMpooXYE9mfWkZevM+rOvvX5tdzf3Xc55SPnzsS5hWAIgBF66fPHZ731Xm+A0ywmec+qL/SxgSn0JTBU1IeohIjQIMZQoTbZsnovZqCFqiCLBHRpCmLTSNL1JdkY9nMxuhWaRHK5AiDG2UTWGKNCgEQghzJo4U21UmxBijLFsUSFwCGHM7B9c3qEznGjoNbHX0J179tkvfOsFm8ygWUq+uerbBcuGnRg75915cLcIIYnoID2QV5sIKKhgcAv0SAYoqbp7s+v7A1Uks95TCEWoNyKtA9CYjL0tur47PEiWAclOz7mmWNNdJYLqVgIHZpZJIAc1b8T2dq+/8OLryea5995TkSJDeLievqLKYtRvfv3Vvf25QKAhW859n3Na9H0mEKKC4giIKtqGKFCjILYxNDR3S2YJuVow7ubiEKpCyKChiW0IQSBBlNnVJ5GAU2EKDxR6JjzTqUpVBkBK82dRRQhaOki50a0M+nP37O5u7szu2d0oNMmAltkN333mhZ6hlH5BglJcKeVjOTSFlurNglJLrpAAG/KOiWVj6sECQJ0lpUiCJM4aaD5yFgB3PvPMd7LTiWy5DmWpxm1x/spR0rZpYwzZcgFFR/NmELBLI96saMpUxru49y++8ML+fupzPxieXAU4i3zWwMtX9j77h18KTWuWSXN633du5mYCBFUagxbQVCCiok3TBI05pa7rzMxLJQskhCACIYJq0DD0UKCqxthImLTtNDaNaNDQaIgUKcWylUE1oKYxF/RYh47oUiZK1Ueqg60qZEkudrNUIr5m1CAXzp578aXLaAr2aSvxnSFL6+2do8GkOvYCgTfF/lB1EmPPtTE3VgqRQoyvvPLy2bPnQpw6xZJZ36XUl9lo7s6ivjSEEGNsRZzMXNYMFokxpkoNWUWAWXYmsrTLyVcuvXXujWuZxjzkCK1kNZenRJs/+P1vXr12lWIp9wBFaDnlvveUA8SzpdTnlMo9UqWdTtrYMNlisRgbHmvQ0cfQaoFQQgDrnYiqakuJhEgQDVFDIxo1RBNk94DSVrIkcYYQGlUFg0oz6P6SAFvvt45b5jhq2osJls0Exnzw+S9+r0cLgRSuY4nYO+ljw6FVk3N1ewdKL0kOoCTHk6NxUvIYbZzxXa4RhweH3/jGN1VVNJoxp1zkRZmdNWT9iEBjaFSDDdWexc5yuqqEGEvnlpEvq3gsjdMBZ+q7xTPPPNenZMnczOuY04HUQIjhwoVrf/T5b8VJMMwhyJb71Dktp2QpxxBT35cemRCJTdNOpgQW8/n88DD1vVm2mp4uKiNlC6gvPlSdA6X9SEFopYDsXvpRFeaWWvkkkJIzNQh8AKohlBsvB1pVx4jfAIl4MbWsjB6iKebPv3j23FvXRRrWcukh/LfCwaNEHPygIw2mOETQhycy6fAINrWKEU6YhhAmkxawyaRp2/b551/Y3b0mamAmDSrQ2C/6nDphEmZI0sC2bZtmKjIWRogw1lBFaCAF4ZcyGk1EggZArNRNggKdRnn1pe+8deEi4OaJMNQm0DXJq43xK1/61v78SqIBama5T26eaNn7ctTNewQJbROaVkLrhr7r+jQ37zgWaIyZT0K6ZWYv3ip9aDFlRKb3glyyB91ywWJBD5BQMqFUVKmqGiNCYf2C4IMwDaU7nIBBSwumgs0V/9PFnCIShDD3nmn/yndeeouxdYoKxBNIcEiBIqqjW0rFaox8zPtRMICBpXmK0xwwodE905PRMs1BI2Kf+sPDwxCCmeXsd9+7fv9DOxfeuNVEEowx5pxLtpQ7m0adHrT0OjJAnbRcOuOmbGwn66qtpfkwu3GQIwJVJTPLBAiIi584sT4JIVsWWXMrGgxghDDE9uy5t77wpa9oDGZlXGJOQNs2hVU0BKdTUKykEDTn3szcDWAIWgoGNKrT3aihQrWqoQTWBBKCsjZAlQEMoJtLgEoAHF77IxWhmz0Vd8jMYD6ayiEoIFacSXotahgdFdWck2qQoTO8uyvTa6+em3/y4ZIaBQDIZRDHMY0ry6mzFbsAWNOv6HRMZ2uT6ZTWZUulxtoZzQiwaWMsOlhViy31wAMPXL527dWXv+nZVJXmIjKZTLLBzIIGN8QY+9SlnEKYVJABTqEziMR69lHzs4qnT0DABgDdkalhPl987IH7tjdmXdfVI8AoaJweYri51//CL/3WwbzXGOlZKbBsdBMVF3dvotYW5FDVaJZSSmauqiFEkQh44SRBSQsstUBaYs9lw7J3TdMCMShE0mCOQCGecxmHhFhSXEVwpIfJ2AhntKpQA7NDQ8oa3q22nAzDLYponSgvnb90/tLBme1Y8s9XCesrE5QHKV1PDWlDLketREuWt2drJ3YehMAtl9ZGZSu63nTI7qW5EZYWzZtnd1U9BKXZYj5fHB7u7e/1fdc0TTnLTdNurG+0TaRb6vuSlZkzJ82aiPT9Xkr9iAAUSotARHe2tiZNm7OlZBCsTVt4mi8O9w9vkHmxSDd291VMFJ/5x//8jTduhmaSegMlhhhU6Jb7RIeKNE2jKjHEECPJvu9zToBr0BAiIKqiWqJaWsMklDLuHIPbX3iRjuIZV+NCNRTTZxSIdTwU2qYNGgZoYrCkVpLUB6yiEkaCjC8TwUBdEgj0bn/+zHdfhbRgBJqh6+fxVQzbkd5ATRSoQ3vIru/eunz5jQsX+pwlBoQoMgk6C2EWwiyWIfNEnR+4v3fr2sVLTShRRYdYtoSOIpr7HGM7nU7VlWTuct93tE7EyHjmzL1PPPH+bz/z7UWXPbkKVTXW6v8OxqDt+uZ0Mpvlawcbm6eefPLRJ9733pxzEzRb9pwUofcutrM/+vy3/viLT69N1w7mC0+9s7RKV7oZE0uiYqx5Qp4XKSWATWnupCKKEAMkABo0kHBYHXoBdbcQQm1uXdQsk1uqkANUg9Bdg5gbSUmMpWukqhhJBAanS3ACblAVp48HpU4kKQn9EkvHqqBilsupKG6uaWjEnn32+ac+/NBjUWkEMql14iJAG2oqCYEO2o5kLfAFHXAJevaFF3T93vc8NFucO3f69Omd7VPUAHEJRBhGbJSKUJEmcy/boXiEjnZvCW6zT55S6lNnmW3bWu7pBgi9+fgnfuiv/o2f/dIXvtjNuwZiQYvojk1TJRUQlGsb4eSp0+/9wPrGxvaJndNtqzGKCM2cyCJ9Ownn37zxT/7pH7bT9ZzNPavCzLNllUbgZA9hCBN69NqdNnOYvBdCU8yCKBqbkJOVCmOt0RonfewNwUHSlvFkQKZLCI1KdEkKEQ1mWQbPx0pfw+quFPppkROeR7MZIgqpTZJJL9CGAF2/iLEB1K12EaSngxs3v/O9Sz/yyY/F/qqw1NndHi8iUcsKSI4F1kCZ7oHDvZvPfOulmx+af/D9j12+cKU/TKfvvje2UQQqGsmc0qJtG3cGw3yRjLDUJzdIgUs4TvQG3K3f27/eNG1sACHdt7ZOTCeTX/3VXz372uslG0NiYOH9vg8hqESKzya6PjsxabbW1jZms/W2aSeTSSgGkobi1sWw/hv/5J/t7S+ma+uWM4Ru2SznnEREA7I53WOYuNPMzJO7F7Cw+JshxNC0GqLlBGeufVNt7J6nQVUlpSRDlnLFsbUEd90sF99mEL8+IBjIXgePiIp4FADKbCmbkQgaAQElaKCyANAigoLkWRKVoLH4khZcRRrDC987e+VAHmzNZehbMLpZo49UuheyjFQqAQ+SXuBZcYuwF55/5tb1Kx/76Ecovjffv+eeMyd2Too0kfRsqUHMaQ6EC+d3Dw58e4NI6HurpK0ZIMsIT7YFIfQIEOi//cxXuj41sYXSKAoVIMSQc85918RWgY2N2cbG+mQyadvZdLI5W9tomlkIkxBi0ECgaTae+e6bTz/9XFxrsi/M++J5m2X3LOpV3rrEiUIs21zUY6ynpAAv0CgSzJyW3UuLKKoUQ7LAcBkSUKo/WQ2llLM4Q1BncvegU/cSh9biX9QAJByljSO0XEGZaLxic3GoKxFS3PuKIQpC6Z1QAsClzMDYOK9dfPPr33rl4R+5J8n10p35CPPWDlGVzZa10DJ8LuiWInLT9Fcunf2Dz1358Mc/+tB9D77++uuLuxd0atAwbSdwtnGS+/jic9+75+6tv/SXf+onfvrTa1stnTp4XRXtWnaBAAnVdrHIJbXdLdMpjGXSAMAQAEU2c8bpbHuytjZdm83Wppubs+naLE7adjKJoYGrNnGR/bOf/bKGNiCyp2fWThQQlG5Og9VaREcIbRM3QjOFNBImGibQxj3kbDkdWOqDIBTbxlCaP9NFPCKLMsBLqznV0iqzQI+ugLpnIKsW9KBobkC0UVVkocFAz06z7DmxANFmZpZqU2iKSBSIIEKiRhVomaZELc1/nJQsGn3xjS9/7XoXootymLLmHKaBu8MAG7KGA5z1HwuU0Xte7N/YO5ynJmrur33ty3/87adfyClfvfzWW2+ei6hlJq6qe3tX+37/Z//Gz25vb23Mb07Cd+ZIMmZYDXJjaVsODRRKU+EaKxUp2HLxGUJo6KVwQyeTtel0NpvNJu2kadumjU0b6SaaY9z8+teePXf2/PrWeimGK0WPwyxCIZFSr6pNbESCGWOYqMbivMCQcjZ0g1BllNYNQCq1LVJjugTKSAKaG5hEAqAiUUSqDx1jSS+pVqtwUNIoyXUqsfQZUy0zGquCL3vh7HO2Wi8tKE02BAqy2NNSIaqCZUo7iWdfffHF1y48vD2xvFjxd99pFbepZKBazjeu37h1+fpDjz62vtFG9+dfeOba7puf+Pgn5n1XKgwqfNq04Ud+9Ac2Nma5t/4gW+ciCj3SmX+0+0upoyog7p5FfCiMGVtqjCiabe+sPfDgPU3TxjgJ2qiWzv8q4pNJmM0mhwf22T/4khdcnlab5QzvF1HVmHNJ5Sqoi+Xcp9S5dZYX3eKgW+ynxUG/uJW6Q5SuT+7m5k6vH+hDxH4UgF47CyxLYVnSEMbMIbqLQrU4oLnaOAJQVDREjU0IpT9UMW3dUu5yPnRfmPVmC2dGCXCoFl1Svgmk0UlLh7e+/eJZ6ERWNnl0t4oxNUZiVokMsHRjFsHB3q2Xz76xeWI7+K1p7K9dvviFL3zh7Btn43hjJLY3T5w8sZNzIkPq9906LUq9xGkEI5mHfS+6p4aAZDRYoVKhWhIZwtmaTKaF1wZXpgh8IeFNnL384oVLl3Y1qJkCyCn5SusOEShCjEq6iKY0TykBJZ+4TpMmITGoAowq4kwiUmINIqkgCXR4Sa+o6TFFgBEwUFXFyuhw9xCKloEonFbK0osNxTKphxXGN8sEx5xnMgDmDoiXnF8wACGEaO45W3HMKSLikOBuAf7yaxfSpz5YB2FAORRbC6TGXaDFDxi9pcHXBgERhNh7SB/+2Pv3XudzL5xrZavfX7zy1sVY6lRBARVwyygPDs2yKAQKIUKdljgUhVZpVH2NqNKSmYWo9cxRIE6REAJlbTYVnaiEIQZhCiHU2So1BP36159Lzhi961LbNqpNv1gMOWwGMYUahc4STnI31kwDAwSIQRtqkKZpQktRClSEcAMHSKBYJ1kU7oCLgFARVQJavDV3iJTMcBEtzQpBlOJlt2oQlIOiqkoh0TSRZHXYEHx02qvOKoXpks2UroiQ2LYRUIXAoZxceO6VZ8999KmHJ+jn5MQlwbVUG5FQFXoAKZKdAQNqTzqVvRZ5FWJsJcyeeN8n1zeap799dt5LUI059/AsUGdfurBRCEhObl7jfVWeVQByLELV6lOSEIfkMmwLK0lGogXO0fX1ddFYMosrBoNERCKINLu7N1999XUR9DlBgVRCe/WYFgYaG/KYm3lf3B6pwdmmjTORoKXiLURC6K4Kc7qbryo1KTaHFX+VLM68ZOblHCAVd48hAFKnJ6uYmYoOY8ZKEqnQETTGWEV0zkawNKYRCXQx7winZGNHTwgNxAdorIaMQuxSyl/4/Lcfvf9Hp9pLdpZ90mF/izgnh4DxAGax9mAlAdUmThRNMjlz7ycm6xvPfOu7+9dy7BcG1xCCQuZzn8/nhfV3r9xSRAZmSyqGKlpdEAcC+6CQ6MxSxRR9yMpALQTVJk7W1zZUA1WpUvqDAQP6L+HK5auLbhFjKBhOLvnrdQhgJXPBlVD7vEAlqopqgMQQ2hinqjHEptRxga4QG+NrpIbAmtbD7EMVvlWS1/Q/kqV0nVSNAFJKIqFpAkAVFgS06uYqFSgCLwUZCDFEosC0wZ2gSnbCVVSlTm4szfdyzoKgQUIQgG3Q115+5TsvPv6p959ENRXHkDBArQEJ1+K+VJZD417HiEOkkUlAQ+kX5psbj3/qk3fTvhxff/mN61euq2Jraz1GOD2E2DSTG1evu1FC8fvGOIYMqQFWLLMhfun04BSI6JCEW/auOMQ3bx5ocxC06UOIMeQYc5g2jTAGkXh9d8+NpYKZAygxAMhQ0dJTsSw3V2nbto0hEEAIJUlKEBhbRCEzvbgQUnFcVkDD3SXEgXuYzQsi7eNsXBENpUs2zUw1AGpGwIeoOYbXQ0TL80DpZh6CRmNPumqMpcGuNqTA1XQOuCCCjTtrc4ba2L8RR5abX/nG9z783j83oZOKZc8TDBOh6Qyj511NQMsFuHSgbadRGmfnCGYeZlsPPP5kvHnt3OFuBu0NBIeK0hHB6f7CwM6dugxDotKSFWwvFGTJSFYlPZTwmjQoDSboQO5tfvb1S7sXD5782PsidJEz+4M8XXTzrYN24qe3Ur6VbNFO1mzRp9wV2ete27PUM0RxUkOUpo3aNE0bC4BFlt3SQGhWKMHszuyCEjClILLsmWe6qYQiGomE6i+VhJ5Qon6qpfEGiuM0GJUSY7WkClGBIYLEAIGqA72i9TK/Hha0ibEh3QxK1SSiZUa7uUMkBBFSRU0ELeLFVy+8de3wkZNTWFcOd7Wn3CACgm6ALmdNS+9+CLOSuSGTABqdgInQXS1LbALWmhAkGEIRwirMqdtfmAhJFcRS0sTBShFZ5ouMS+sENANoRo1lziHKWAvIou/6s+df2d45sTFbm80ma7Nubd1maztxb7IWrdU+shWNvR8QUGmAADFgnMbS0zM0rM8222aKod1QCLXvMkQL1oCK1NeIW2E4s6xBQlCzUsNYaiKNriIBWtuJiNRcFAxpzyEAoqhpZRjmVSyTVUiaWYwluugqqhBn6Y5c25UF1cwgADy5GCGCUNO4oEA2B6h5vvfqqxfec/JxsLSFK2kUWs5Z5bBhOUu6es4p17oNrSg5YaVxP93jdIJZ40K60GFmYq42tGwRKTKhRteWknfwngeRRfccBLWKSl0hQaSE5MnsknPKV165cU2gIlGjqofYapiqtu2sVTSLxX7bbjbNtOsWLkZmImGwMQQudMspp346mYTQuCmpRA8VlUACloryErIUhln2EGI5IYNgLHi5u7sKIKWJRZ/NVWNUqmpRrgV9dJq4D259vd/yc5iJDVG6m2ooYqbgu9VvlEg6kHK/yH0XgtJV40SjAmKWpaYPIQYP4m+8edU++WgthvdRLXI0ypYRSSdFzGGGUoUbYy18coBV+jFub7SbUwsqyaVz271p1xcwNl59L4f0gjB4riXR5UjEY3BsKUAIEiW4s5gP7p5SIrKCkyZOgsSYmyhtCE0TYiuhEY2Tg26HB908LTKaJs5EYp8OLHcAC8rm5pqVZLbDG6nv88Hm5skgUzCWSeoaQtniWkjnni0VJZKzDzUNJXlDCZciUSVYGVckECXgOacw5FjVlA94tbxUwMCVVdPMBVqPeI0vlSoKCMgGFGju+1uL/hYhIjNBdBLZDBQJMYAoB8WDYm+vTzk3LFmSXlN2WECZY0zshHouKaYODI3lisdfXueI2rRtm9zD9Zt64coeMTFNpMQg2XJxJ1CmE5YeTCwRCAFIGBCEpGSXBijolAoQQumxo9k6ZdGf6g5LpZOUE8Y88/V7DuX0frIeBqrljllD1OlkrQNS6mgsw9AJARXM5GJvrw9hMplEwiOiiJZ2W05xF3MxE5U4+BiZMHjB+nv3eljJKFDAVJUINR2vaCJhqU8xNzMGjQgg+pxNJRSLTEtTG3FzAC3FgdKkuhS5KAlhEMk5z/dvXTfLsZkhNBomUBBSa/PFVBCCQOkWg2mGRIoMw0cLExFAjRaXZwtG7czZUjZRF0QJ4nW2b42BUWNs0Eyb3avzJu5sn5Td6weiLmhL8Rwy+2xS28KawOSIK3xcFReXkWoswXFyGj2G0AS2rapmVZm0YTrRAFK2+3DCdFvbbrJm2jRdv+i7fSbE2MQ4E0xzWpgvajN6VqBBwJz6EDoNwT0JUNIhUsru0KAiXAJMQhq8VkVX96Miz6IiNUKlqqgobEBpm+/FzW0K5lysfKeVZFtYMPMS6yS9pLzTKKoxxpQSABEj8mK+n3Nf0FWVjBCbMFGNIkGgGmqX2DLmc2/v1nwhTVAwjV36HRiCSKWuzwl4SRzI1ved0yClkbGNkrykcMY3LvjNLbvr1Nb+9Zt7hwnaQOmOIAiqURpjQTtlsMyHEquhDoYDbDkIEM9IJWGgUW/b3DbaRJm21iqbBtMWTUMP29e7k5evp9DeNJrQ6dlST/YQdZecoRKaphFYTubsnSWwLk0IUZHToZoSsyEfmZlJtZjHFCXdc87Fl3MmqU2ZApbIcy4KVUuFaam988FidZOypUwgBSEElIp1H5p5Wdlq+qjFioAsJSzO3HUHZklF6UZLmQfmXd/HGNoY12JoNUSv2ysQ271x5cLFve1HttGbDjPCzItv4qghjaFgXODObJlwECFS1GrgslaqS7ywu399wb2OajH1dIbsyb0PhGhJ+w3G3ilgAFVlwDEcTqFqAcNLq56U3ACLAGUijYglD8giGrusmEgZW9BlQjZee+vW9Vv7EYdepcJg+4qIpuKAaElpBmp5SwXeabk3Jgij5gEpF4ebIaXSTU2GxPcAmHMhpfkSGw1aYUTxmikgAq8IsWoghuwAUSfKmBxBW8QjSFExJBU4mUkVZ4hkaWQgYysas5TTYgBFQTo90ROFCTFoF+M0pFCiEABVpCc/97k/DvbEtEA7IFnG+TrhoDuGVo0UUR5cO7+7ez0nJ31xuP/GG6+zzwVAUoSrV3f/f7ldAZlSG2LaAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im.to_thumb(160)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Biwi dataset web site explains the format of the pose text file associated with each image, which shows the location of the center of the head. The details of this aren't important for our purposes, so we'll just show the function we use to extract the head center point:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cal = np.genfromtxt(path/'01'/'rgb.cal', skip_footer=6)\n",
+ "def get_ctr(f):\n",
+ " ctr = np.genfromtxt(img2pose(f), skip_header=3)\n",
+ " c1 = ctr[0] * cal[0][0]/ctr[2] + cal[0][2]\n",
+ " c2 = ctr[1] * cal[1][1]/ctr[2] + cal[1][2]\n",
+ " return tensor([c1,c2])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This function returns the coordinates as a tensor of two items:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([384.6370, 259.4787])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "get_ctr(img_files[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can pass this function to `DataBlock` as `get_y`, since it is responsible for labeling each item. We'll resize the images to half their input size, just to speed up training a bit.\n",
+ "\n",
+ "One important point to note is that we should not just use a random splitter. The reason for this is that the same person appears in multiple images in this dataset — but we want to ensure that our model can generalise to people that it hasn't seen yet. Each folder in the dataset contains the images for one person. Therefore, we can create a splitter function which returns true for just one person, resulting in a validation set containing just that person's images.\n",
+ "\n",
+ "The only other difference to previous data block examples is that the second block is a `PointBlock`. This is necessary so that fastai knows that the labels represent coordinates; that way, it knows that when doing data augmentation, it should do the same augmentation to these coordinates as it does to the images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "biwi = DataBlock(blocks=(ImageBlock, PointBlock),\n",
+ " get_items=get_image_files,\n",
+ " get_y=get_ctr,\n",
+ " splitter=FuncSplitter(lambda o: o.parent.name=='13'),\n",
+ " batch_tfms=[*aug_transforms(size=(240,320)), Normalize.from_stats(*imagenet_stats)])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> important: We're not aware of other libraries (except for fastai) that automatically and correctly apply data augmentation to coordinates. So if you're working with another library, you may need to disable data augmentation for these kinda of problems."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before doing any modeling, we should look at our data to confirm it seems OK."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls = biwi.dataloaders(path)\n",
+ "dls.show_batch(max_n=9, figsize=(8,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's looking good! As well as looking at the batch visually, it's a good idea to also look at the underlying tensors (especially as a student, it will help clarify your understanding of what your model is really seeing)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([64, 3, 240, 320]), torch.Size([64, 1, 2]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xb,yb = dls.one_batch()\n",
+ "xb.shape,yb.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Make sure that you understand *why* these are the shapes for our mini-batches.\n",
+ "\n",
+ "Here's an example of one row from the dependent variable:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0.0111, 0.1810]], device='cuda:5')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "yb[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, we haven't had to use a separate *image regression* application; all we've had to do is label the data, and tell fastai what kind of data the independent and dependent variables represent."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training a model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As usual we can use `cnn_learner` to create our `Learner`. Remember way back in <> how we used `y_range` to tell fastai the range of our targets? We'll do the same here; coordinates in fastai and PyTorch are always rescaled between -1 and +1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = cnn_learner(dls, resnet18, y_range=(-1,1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`y_range` is implemented in fastai using `sigmoid_range`, which is defined as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid_range(x, lo, hi): return torch.sigmoid(x) * (hi-lo) + lo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is set as the final layer of the model, if `y_range` is defined. Take a moment to think about what this function does, and why it forces the model to output activations in the range `(low,high)`.\n",
+ "\n",
+ "Here's what it looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_function(partial(sigmoid_range,lo=-1,hi=1), min=-4, max=4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We didn't specify a loss function, which means we're getting whatever fastai chooses as the default. Let's see what it picked for us:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "FlattenedLoss of MSELoss()"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dls.loss_func"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This makes sense, since when coordinates are used a dependent variable, most of the time we're likely to be trying to predict something as close as possible; that's basically what `MSELoss` (mean-squared error loss) does. If you want to use a different loss function, you can pass it to `cnn_learner` using the `loss_func` parameter.\n",
+ "\n",
+ "Note also that we didn't specify any metrics. That's because the MSE is already a useful metric for this task (although it's probably more interpretable after we take the square root). \n",
+ "\n",
+ "We can pick a good learning rate with the Learning Rate Finder:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.lr_find()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll try an LR of `2e-2`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lr = 2e-2\n",
+ "learn.fit_one_cycle(5, lr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generally when we run this we get a loss of around `0.0001`, which corresponds to an average coordinate prediction error of:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.01"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "math.sqrt(0.0001)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This sounds very accurate! But most importantly, we should have a *look* at our results with `Learner.show_results`. The left side is actual (*ground truth*) and the right side are our model's predictions. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.045840 | \n", + "0.012957 | \n", + "00:36 | \n", + "
| 1 | \n", + "0.006369 | \n", + "0.001853 | \n", + "00:36 | \n", + "
| 2 | \n", + "0.003000 | \n", + "0.000496 | \n", + "00:37 | \n", + "
| 3 | \n", + "0.001963 | \n", + "0.000360 | \n", + "00:37 | \n", + "
| 4 | \n", + "0.001584 | \n", + "0.000116 | \n", + "00:36 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.show_results(ds_idx=1, max_n=3, figsize=(6,8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's quite amazing that with just a few minutes of computation we've created such an accurate key points model, and without any special domain-specific application. This is the power of building on flexible APIs, and using transfer learning! It's particularly striking that we've been able to use transfer learning so effectively even between totally different tasks; our pretrained model was trained to do image classification, and we fine-tuned for image regression."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusion"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In problems that are at first glance completely different (single-label classification, multi-label classification and regression) we end up using the same model with just different numbers of outputs. The different directions of those trainings is determined by the loss function, which is the one thing that changes. That's why it simportant to double-check your are using the right loss function for your problem.\n",
+ "\n",
+ "In fastai, the library will automatically try to pick the right one from the data you built, but if you are using pure PyTorch to build your `DataLoader`s, make sure you think hard when you have to decide on your loss function, and remember that you most probably want\n",
+ "\n",
+ "- `nn.CrossEntropyLoss` for single-label classification\n",
+ "- `nn.BCEWithLogitsLoss` for multi-label classification\n",
+ "- `nn.MSELoss` for regression"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. how could multi-label classification improve the usability of the bear classifier?\n",
+ "1. How do we encode the dependent variable in a multi-label classification problem?\n",
+ "1. How do you access the rows and columns of a DataFrame as if it was a matrix?\n",
+ "1. How do you get a column by name from a DataFrame?\n",
+ "1. What is the difference between a dataset and DataLoader?\n",
+ "1. What does a Datasets object normally contain?\n",
+ "1. What does a DataLoaders object normally contain?\n",
+ "1. What does lambda do in Python?\n",
+ "1. What are the methods to customise how the independent and dependent variables are created with the data block API?\n",
+ "1. Why is softmax not an appropriate output activation function when using a one hot encoded target?\n",
+ "1. Why is nll_loss not an appropriate loss function when using a one hot encoded target?\n",
+ "1. What is the difference between `nn.BCELoss` and `nn.BCEWithLogitsLoss`?\n",
+ "1. Why can't we use regular accuracy in a multi-label problem?\n",
+ "1. When is it okay to tune an hyper-parameter on the validation set?\n",
+ "1. How is `y_range` implemented in fastai? (See if you can implement it yourself and test it without peaking!)\n",
+ "1. What is a regression problem? What loss function should you use for such a problem?\n",
+ "1. What do you need to do to make sure the fastai library applies the same data augmentation to your inputs images and your target point coordinates?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Read a tutorial about pandas DataFrames and experiment with a few methods that look interesting to you. Have a look at the book website for recommended tutorials.\n",
+ "1. Retrain the bear classifier using multi-label classification. See if you can make it work effectively with images that don't contain any bears, including showing that information in the web application. Try an image with two different kinds of bears. Check whether the accuracy on the single label dataset is impacted using multi-label classification."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/07_sizing_and_tta.ipynb b/07_sizing_and_tta.ipynb
new file mode 100644
index 000000000..05f0e0b7f
--- /dev/null
+++ b/07_sizing_and_tta.ipynb
@@ -0,0 +1,1018 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_sizing_and_tta]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Training a state-of-the-art model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This chapter introduces more advanced techniques for training an image classification model and get state-of-the-art results. You can skip it if you want to learn more about other applications of deep learning and come back to it later--nothing in this chapter will be assumed in later chapters.\n",
+ "\n",
+ "We will look at powerful data augmentation techniques, the *progressive resizing* approach and test time augmentation. To show all of this, we are going to train a model from scratch (not transfer learning) using a subset of ImageNet called [Imagenette](https://github.com/fastai/imagenette). It contains ten very different categories from the original ImageNet dataset, making for quicker training when we want to experiment.\n",
+ "\n",
+ "This is going to be much harder to do well than our previous datasets because we're using full-size, full-color images, which are photos of objects of different sizes, in different orientations, in different lighting, and so forth... So in this chapter we're going to introduce some important techniques for getting the most out of your dataset, especially when you're training from scratch, or transfer learning to a very different kind of dataset to what the pretrained model used."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Imagenette"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When fast.ai first started there were three main datasets that people used for building and testing computer vision models:\n",
+ "\n",
+ "- *ImageNet*: 1.3 million images of various sizes around 500 pixels across, in 1000 categories, which took a few days to train\n",
+ "- *MNIST*: 50,000 28x28 pixel greyscale handwritten digits\n",
+ "- *CIFAR10*: 60,000 32x32 colour images in 10 classes\n",
+ "\n",
+ "The problem is that the small datasets didn't actually generalise effectively to the large ImageNet dataset. The approaches that worked well on ImageNet generally had to be developed and trained on ImageNet. This led to many people believing that only researchers with access to giant computing resources could effectively contribute to developing image classification algorithms.\n",
+ "\n",
+ "We thought that seemed very unlikely to be true. We had never actually seen a study that showed that ImageNet happen to be exactly the right size, and that other datasets could not be developed which would provide useful insights. So we thought we would try to create a new dataset which researchers could test their algorithms on quickly and cheaply, but which would also provide insights likely to work on the full ImageNet dataset.\n",
+ "\n",
+ "About three hours later we had created Imagenette. We selected 10 classes from the full ImageNet which look very different to each other. We hope that it would be possible to create a classifier that worked to recognise these classes quickly and cheaply. When we tried it out, we discovered we were right. We then tried out a few algorithmic tweaks to see how they impacted Imagenette, found some which worked pretty well, and tested them on ImageNet as well — we were very pleased to find that our tweaks worked well on ImageNet too!\n",
+ "\n",
+ "There is an important message here: the dataset you get given is not necessarily the dataset you want; it's particularly unlikely to be the dataset that you want to do your development and prototyping in. You should aim to have an iteration speed of no more than a couple of minutes — that is, when you come up with a new idea you want to try out, you should be able to train a model and see how it goes within a couple of minutes. If it's taking longer to do an experiment, think about how you could cut down your dataset, or simplify your model, to improve your experimentation speed. The more experiments you can do, the better!\n",
+ "\n",
+ "Let's get started with this dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.vision.all import *\n",
+ "path = untar_data(URLs.IMAGENETTE)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First we'll get our dataset into a `DataLoaders` object, using the *presizing* trick we saw in <>:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dblock = DataBlock(blocks=(ImageBlock(), CategoryBlock()),\n",
+ " get_items=get_image_files,\n",
+ " get_y=parent_label,\n",
+ " item_tfms=Resize(460),\n",
+ " batch_tfms=aug_transforms(size=224, min_scale=0.75))\n",
+ "dls = dblock.dataloaders(path, bs=64)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " ...and do a training that will serve as a baseline:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = xresnet50()\n",
+ "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n",
+ "learn.fit_one_cycle(5, 3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's a good baseline, since we are not using a pretrained model, but we can do better. When working with models that are being trained from scratch, or fine-tuned to a very different dataset to that used for the pretraining, there are additional techniques that are really important. In the rest of the chapter we'll consider some of the key approaches you'll want to be familiar with. The first one is normalizing your data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Normalization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When training a model, it helps if your input data is normalized, that is, as a mean of 0 and a standard deviation of 1. But most images and computer vision libraries will use values between 0 and 255 for pixels, or between 0 and 1; in either case, your data is not going to have a mean of zero and a standard deviation of one.\n",
+ "\n",
+ "Let's grab a batch of our data and look at those values, by averaging over all axes except for the channel axis, which is axis 1:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(TensorImage([0.4842, 0.4711, 0.4511], device='cuda:5'),\n",
+ " TensorImage([0.2873, 0.2893, 0.3110], device='cuda:5'))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x,y = dls.one_batch()\n",
+ "x.mean(dim=[0,2,3]),x.std(dim=[0,2,3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we expected, its mean and standard deviation is not very close to the desired values of zero and one. This is easy to do in fastai by adding the `Normalize` transform. This acts on a whole mini batch at once, so you can add it to the `batch_tfms` section of your data block. You need to pass to this transform the mean and standard deviation that you want to use; fastai comes with the standard ImageNet mean and standard deviation already defined. (If you do not pass any statistics to the Normalize transform, fastai will automatically calculate them from a single batch of your data.)\n",
+ "\n",
+ "Let's add this transform (using `imagenet_stats` as Imagenette is a subset of ImageNet) and have a look at one batch now:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_dls(bs, size):\n",
+ " dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n",
+ " get_items=get_image_files,\n",
+ " get_y=parent_label,\n",
+ " item_tfms=Resize(460),\n",
+ " batch_tfms=[*aug_transforms(size=size, min_scale=0.75),\n",
+ " Normalize.from_stats(*imagenet_stats)])\n",
+ " return dblock.dataloaders(path, bs=bs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dls = get_dls(64, 224)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(TensorImage([-0.0787, 0.0525, 0.2136], device='cuda:5'),\n",
+ " TensorImage([1.2330, 1.2112, 1.3031], device='cuda:5'))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x,y = dls.one_batch()\n",
+ "x.mean(dim=[0,2,3]),x.std(dim=[0,2,3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's check how normalization helps training our model here:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = xresnet50()\n",
+ "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n",
+ "learn.fit_one_cycle(5, 3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although it only helped a little here, normalization becomes especially important when using pretrained models. The pretrained model only knows how to work with data of the type that it has seen before. If the average pixel was zero in the data it was trained with, but your data has zero as the minimum possible value of a pixel, then the model is going to be seeing something very different to what is intended! \n",
+ "\n",
+ "This means that when you distribute a model, you need to also distribute the statistics used for normalization, since anyone using it for inference, or transfer learning, will need to use the same statistics. By the same token, if you're using a model that someone else has trained, make sure you find out what normalization statistics they used, and match them.\n",
+ "\n",
+ "We didn't have to handle normalization in previous chapters because when using a pretrained model through `cnn_learner`, the fastai library automatically adds the proper `Normalize` transform; the model has been pretrained with certain statistics in `Normalize` (usually coming from the ImageNet dataset), so the library can fill those for you. Note that this only applies with pretrained models, which is why we need to add it manually here, when training from scratch.\n",
+ "\n",
+ "All our training up until now have been done at size 224. We could have begun training at a smaller size before going to that. This is called *progressive resizing*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Progressive resizing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When fast.ai and its team of students [won the DAWNBench competition](https://www.theverge.com/2018/5/7/17316010/fast-ai-speed-test-stanford-dawnbench-google-intel), one of the most important innovations was something very simple: start training using small images, and end training using large images. By spending most of the epochs training with small images, training completed much faster. By completing training using large images, the final accuracy was much higher. We call this approach *progressive resizing*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: progressive resizing: Gradually using larger and larger images as you train."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we have seen, the kinds of features that are learned by convolutional neural networks are not in any way specific to the size of the image — early layers find things like edges and gradients, and later layers may find things like noses and sunsets. So, when we change image size in the middle of training, it doesn't mean that we have two find totally different parameters for our model.\n",
+ "\n",
+ "But clearly there are some differences between small images and big ones, so we shouldn't expect our model to continue working exactly as well, with no changes at all. Does this remind you of something? When we developed this idea, it reminded us of transfer learning! We are trying to get our model to learn to do something a little bit different to what it has learned to do before. Therefore, we should be able to use the `fine_tune` method after we resize our images.\n",
+ "\n",
+ "There is an additional benefit to progressive resizing: it is another form of data augmentation. Therefore, you should expect to see better generalisation of your models that are trained with progressive resizing.\n",
+ "\n",
+ "To implement progressive resizing it is most convenient if you first create a `get_dls` function which takes an image size and a batch size, and returns your `DataLoaders`:\n",
+ "\n",
+ "Now you can create your `DataLoaders` with a small size, and `fit_one_cycle` in the usual way, for a few less epochs than you might otherwise do:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls = get_dls(128, 128)\n",
+ "learn = Learner(dls, xresnet50(), loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n",
+ "learn.fit_one_cycle(4, 3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then you can replace the DataLoaders inside the Learner, and `fine_tune`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.dls = get_dls(64, 224)\n",
+ "learn.fine_tune(5, 1e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, we're getting much better performance, and the initial training on small images was much faster on each epoch.\n",
+ "\n",
+ "You can repeat the process of increasing size and training more epochs as many times as you like, for as big an image as you wish--but of course, you will not get any benefit by using an image size larger than the size of your images on disk.\n",
+ "\n",
+ "Note that for transfer learning, progressive resizing may actually hurt performance. This would happen if your pretrained model was quite similar to your transfer learning task and dataset, and was trained on similar sized images, so the weights don't need to be changed much. In that case, training on smaller images may damage the pretrained weights.\n",
+ "\n",
+ "On the other hand, if the transfer learning task is going to be on images that are of different sizes, shapes, or style to those used in the pretraining tasks, progressive resizing will probably help. As always, the answer to \"does it help?\" is \"try it!\".\n",
+ "\n",
+ "Another thing we could try is applying data augmentation to the validation set: up until now, we have only applied it on the training set and the validation set always gets the same images. But maybe we could try to make predictions for a few augmented versions of the validation set and average them. This is called *test time augmentation*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Test time augmentation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have been using random cropping as a way to get some useful data augmentation, which leads to better generalisation, and results in a need for less training data. When we use random cropping, fastai will automatically use centre-cropping for the validation set — that is, it will select the largest square area it can in the centre of the image, such that it does not go past the image edges.\n",
+ "\n",
+ "This can often be problematic. For instance, in a multi-label dataset sometimes there are small objects towards the edges of an image; these could be entirely cropped out by the centre cropping. Even for datasets such as the pet breed classification data we're working on now, it's possible that some critical feature necessary for identifying the correct breed, such as the colour of the nose, could be cropped out.\n",
+ "\n",
+ "One solution to this is to avoid random cropping entirely. Instead, we could simply squish or stretch the rectangular images to fit into a square space. But then we miss out on a very useful data augmentation, and we also make the image recognition more difficult for our model, because it has to learn how to recognise squished and squeezed images, rather than just correctly proportioned images.\n",
+ "\n",
+ "Another solution is to not just centre crop for validation, but instead to select a number of areas to crop from the original rectangular image, pass each of them through our model, and take the maximum or average of the predictions. In fact, we could do this not just for different crops, but for different values across all of our test time augmentation parameters. This is known as *test time augmentation* (TTA)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: test time augmentation (TTA): during inference or validation, creating multiple versions of each image, using data augmentation, and then taking the average or maximum of the predictions for each augmented version of the image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TK pic of TTA"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Depending on the dataset, test time augmentation can result in dramatic improvements in accuracy. It does not change the time required to train at all, but will increase the amount of time for validation or inference by the number of test time augmented images requested. By default, fastai will use the unaugmented centre crop image, plus four randomly augmented images.\n",
+ "\n",
+ "You can pass any DataLoader to fastai's `tta` method; by default, it will use your validation set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "0.8737863898277283"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds,targs = learn.tta()\n",
+ "accuracy(preds, targs).item()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see, using TTA gives us good a boost of performance, with no additional training required. However, it does make inference slower--if you're averaging 5 images for TTA, inference will be 5x slower.\n",
+ "\n",
+ "Data augmentation helps train better models as we saw. Let's now focus on a new data augmentation technique called *Mixup*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Mixup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Mixup, introduced in the 2017 paper [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412), is a very powerful data augmentation technique which can provide dramatically higher accuracy, especially when you don't have much data, and don't have a pretrained model that was trained on data similar to your dataset. The paper explains: \"While data augmentation consistently leads to improved generalization, the procedure is dataset-dependent, and thus requires the use of expert knowledge.\" For instance, it's common to flip images as part of data augmentation, but should you flip only horizontally, or also vertically? The answer is that it depends on your dataset. In addition, if flipping (for instance) doesn't provide enough data augmentation for you, you can't \"flip more\". It's helpful to have data augmentation techniques where you can \"dial up\" or \"dial down\" the amount of data augmentation, to see what works best for you.\n",
+ "\n",
+ "Mixup works as follows, for each image:\n",
+ "\n",
+ "1. Select another image from your dataset at random\n",
+ "1. Pick a weight at random\n",
+ "1. Take a weighted average (using the weight from step 2) of the selected image with your image; this will be your independent variable\n",
+ "1. Take a weighted average (with the same weight) of this image's labels with your image's labels; this will be your dependent variable\n",
+ "\n",
+ "In pseudo-code, we're doing (where `t` is the weight for our weighted average):\n",
+ "\n",
+ "```\n",
+ "image2,target2 = dataset[randint(0,len(dataset)]\n",
+ "t = random_float(0.5,1.0)\n",
+ "new_image = t * image1 + (1-t) * image2\n",
+ "new_target = t * target1 + (1-t) * target2\n",
+ "```\n",
+ "\n",
+ "For this to work, our targets need to be one-hot encoded. The paper describes this using these equations (where $\\lambda$ is the same as `t` in our code above):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"An](\"images/att_00063.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: Papers and math"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're going to be looking at more and more research papers from here on in the book. Now that you have the basic jargon, you might be surprised to discover how much of them you can understand, with a little practice! One issue you'll notice is that greek letters, such as $\\lambda$, appear in most papers. It's a very good idea to learn the names of all the greek letters, since otherwise it's very hard to read the papers to yourself, and remember them, and it's also hard to read code based on them (since code often uses the name of the greek letter spelled out, such as `lambda`).\n",
+ "\n",
+ "The bigger issue with papers is that they use math, instead of code, to explain what's going on. If you don't have much of a math background, this will likely be intimidating and confusing at first. But remember: what is being shown in the math, is something that will be implemented in code. It's just another way of talking about the same thing! After reading a few papers, you'll pick up more and more of the notation. If you don't know what a symbol is, try looking it up on Wikipedia's [list of mathematical symbols](https://en.wikipedia.org/wiki/List_of_mathematical_symbols) or draw it on [Detexify](http://detexify.kirelabs.org/classify.html) which (using machine learning!) will find the name of your hard-drawn symbol. Then you can search online for that name to find out what it's for."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's what it looks like when we take a *linear combination* of images, as done in Mixup:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.583403 | \n", + "2.064317 | \n", + "0.401792 | \n", + "01:03 | \n", + "
| 1 | \n", + "1.208877 | \n", + "1.260106 | \n", + "0.601568 | \n", + "01:02 | \n", + "
| 2 | \n", + "0.925265 | \n", + "1.036154 | \n", + "0.664302 | \n", + "01:03 | \n", + "
| 3 | \n", + "0.730190 | \n", + "0.700906 | \n", + "0.777819 | \n", + "01:03 | \n", + "
| 4 | \n", + "0.585707 | \n", + "0.541810 | \n", + "0.825243 | \n", + "01:03 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.632865 | \n", + "2.250024 | \n", + "0.391337 | \n", + "01:02 | \n", + "
| 1 | \n", + "1.294041 | \n", + "1.579932 | \n", + "0.517177 | \n", + "01:02 | \n", + "
| 2 | \n", + "0.960535 | \n", + "1.069164 | \n", + "0.657207 | \n", + "01:04 | \n", + "
| 3 | \n", + "0.730220 | \n", + "0.767433 | \n", + "0.771845 | \n", + "01:05 | \n", + "
| 4 | \n", + "0.577889 | \n", + "0.550673 | \n", + "0.824496 | \n", + "01:06 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.902943 | \n", + "2.447006 | \n", + "0.401419 | \n", + "00:30 | \n", + "
| 1 | \n", + "1.315203 | \n", + "1.572992 | \n", + "0.525765 | \n", + "00:30 | \n", + "
| 2 | \n", + "1.001199 | \n", + "0.767886 | \n", + "0.759149 | \n", + "00:30 | \n", + "
| 3 | \n", + "0.765864 | \n", + "0.665562 | \n", + "0.797984 | \n", + "00:30 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.985213 | \n", + "1.654063 | \n", + "0.565721 | \n", + "01:06 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.706869 | \n", + "0.689622 | \n", + "0.784541 | \n", + "01:07 | \n", + "
| 1 | \n", + "0.739217 | \n", + "0.928541 | \n", + "0.712472 | \n", + "01:07 | \n", + "
| 2 | \n", + "0.629462 | \n", + "0.788906 | \n", + "0.764003 | \n", + "01:07 | \n", + "
| 3 | \n", + "0.491912 | \n", + "0.502622 | \n", + "0.836445 | \n", + "01:06 | \n", + "
| 4 | \n", + "0.414880 | \n", + "0.431332 | \n", + "0.863331 | \n", + "01:06 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: Papers and math"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're going to be looking at more and more research papers from here on in the book. Now that you have the basic jargon, you might be surprised to discover how much of them you can understand, with a little practice! One issue you'll notice is that greek letters, such as $\\lambda$, appear in most papers. It's a very good idea to learn the names of all the greek letters, since otherwise it's very hard to read the papers to yourself, and remember them, and it's also hard to read code based on them (since code often uses the name of the greek letter spelled out, such as `lambda`).\n",
+ "\n",
+ "The bigger issue with papers is that they use math, instead of code, to explain what's going on. If you don't have much of a math background, this will likely be intimidating and confusing at first. But remember: what is being shown in the math, is something that will be implemented in code. It's just another way of talking about the same thing! After reading a few papers, you'll pick up more and more of the notation. If you don't know what a symbol is, try looking it up on Wikipedia's [list of mathematical symbols](https://en.wikipedia.org/wiki/List_of_mathematical_symbols) or draw it on [Detexify](http://detexify.kirelabs.org/classify.html) which (using machine learning!) will find the name of your hard-drawn symbol. Then you can search online for that name to find out what it's for."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's what it looks like when we take a *linear combination* of images, as done in Mixup:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "#id mixup_example\n",
+ "#caption Mixing a chruch and a gas station\n",
+ "#alt An image of a church, a gas station and the two mixed up.\n",
+ "church = PILImage.create(get_image_files_sorted(path/'train'/'n03028079')[0])\n",
+ "gas = PILImage.create(get_image_files_sorted(path/'train'/'n03425413')[0])\n",
+ "church = church.resize((256,256))\n",
+ "gas = gas.resize((256,256))\n",
+ "tchurch = tensor(church).float() / 255.\n",
+ "tgas = tensor(gas).float() / 255.\n",
+ "\n",
+ "_,axs = plt.subplots(1, 3, figsize=(12,4))\n",
+ "show_image(tchurch, ax=axs[0]);\n",
+ "show_image(tgas, ax=axs[1]);\n",
+ "show_image((0.3*tchurch + 0.7*tgas), ax=axs[2]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The third image is built by adding 0.3 times the first one and 0.7 times the second. In this example, should the model predict church? gas station? The right answer is 30% church and 70% gas station since that's what we'll get if we take the linear combination of the one-hot encoded targets. For instance, if *church* has for index 2 and *gas station* as for index 7, the one-hot-encoded representations are\n",
+ "```\n",
+ "[0, 0, 1, 0, 0, 0, 0, 0, 0, 0] and [0, 0, 0, 0, 0, 0, 0, 1, 0, 0]\n",
+ "```\n",
+ "(since we have ten classes in total) so our final target is\n",
+ "```\n",
+ "[0, 0, 0.3, 0, 0, 0, 0, 0.7, 0, 0]\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This all done for us inside fastai by adding a `Callback` to our `Learner`. `Callback`s are what is used inside fastai to inject custom behavior in the training loop (like a learning rate schedule, or training in mixed precision). We'll be learning all about callbacks, including how to make your own, in <>. For now, all you need to know is that you use the `cbs` parameter to `Learner` to pass callbacks.\n",
+ "\n",
+ "Here is how you train a model with Mixup:\n",
+ "\n",
+ "```\n",
+ "model = xresnet50()\n",
+ "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy, cbs=Mixup)\n",
+ "learn.fit_one_cycle(5, 3e-3)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So what happens if we train a model where our data is \"mixed up\" in this way? Clearly, it's going to be harder to train, because it's harder to see what's in each image. And the model has to predict two labels per image, rather than just one, as well as figuring out how much each one is weighted. Overfitting seems less likely to be a problem, because we're not showing the same image each epoch, but are instead showing a random combination of two images.\n",
+ "\n",
+ "Mixup requires far more epochs to train to a better accuracy, compared to other augmentation approaches we've seen. You can try training Imagenette with and without Mixup by using the `examples/train_imagenette.py` script in the fastai repo. At the time of writing, the leaderboard in the [Imagenette repo](https://github.com/fastai/imagenette/) is showing that mixup is used for all leading results for trainings of >80 epochs, and for few epochs Mixup is not being used. This is inline with our experience of using Mixup too.\n",
+ "\n",
+ "One of the reasons that mixup is so exciting is that it can be applied to types of data other than photos. In fact, some people have even shown good results by using mixup on activations *inside* their model, not just on inputs--these allows Mixup to be used for NLP and other data types too.\n",
+ "\n",
+ "There's another subtle issue that Mixup deals with for us, which is that it's not actually possible with the models we've seen before for our loss to ever be perfect. The problem is that our labels are ones and zeros, but softmax and sigmoid *never* can equal one or zero. So when we train our model, it causes it to push our activations ever closer to zero and one, such that the more epochs we do, the more extreme our activations become.\n",
+ "\n",
+ "With Mixup, we no longer have that problem, because our labels will only be exactly one or zero if we happen to \"mix\" with another image of the same class. The rest of the time, our labels will be a linear combination, such as the 0.7 and 0.3 we got in the church and gas station example above.\n",
+ "\n",
+ "One issue with this, however, is that Mixup is \"accidentally\" making the labels bigger than zero, or smaller than one. That is to say, we're not *explicitly* telling our model that we want to change the labels in this way. So if we want to change to make the labels closer, or further away, from zero and one, we have to change the amount of Mixup--which also changes the amount of data augmentation, which might not be what we want. There is, however, a way to handle this more directly, which is to use *label smoothing*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Label smoothing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the theoretical expression of the loss, in classification problems, our targets are one-hot encoded (in practice we tend to avoid doing it to save memory, but what we compute is the same loss as if we had used one-hot encoding). That means the model is trained to return 0 for all categories but one, for which it is trained to return 1. Even 0.999 is not *good enough*, the model will get gradients and learn to predict activations that are even more confident. This encourages overfitting and gives you at inference time a model that is not going to give meaningful probabilities: it will always say 1 for the predicted category even if it's not too sure, just because it was trained this way.\n",
+ "\n",
+ "It can become very harmful if your data is not perfectly labeled. In the bear classifier we studied in <>, we saw that some of the images were mislabeled, or contained two different kinds of bears. In general, your data will never be perfect. Even if the labels were manually produced by humans, they could make mistakes, or have differences of opinions on images harder to label.\n",
+ "\n",
+ "Instead, we could replace all our `1`s by a number a bit less than `1`, and our `0`s by a number a bit more than `0`, and then train. This is called *label smoothing*. By encouraging your model to be less confident, label smoothing will make your training more robust, even if there is mislabeled data, and will produce a model that generalizes better at inference.\n",
+ "\n",
+ "This is how label smoothing works in practice: we start with one-hot encoded labels, then replace all zeros by $\\frac{\\epsilon}{N}$ (that's the greek letter *epsilon*, which is what was used in the [paper which introduced label smoothing](https://arxiv.org/abs/1512.00567), and is used in the fastai code) where $N$ is the number of classes and $\\epsilon$ is a parameter (usually 0.1, which would mean we are 10% unsure of our labels). Since you want the labels to add up to 1, replace the 1 by $1-\\epsilon + \\frac{\\epsilon}{N}$. This way, we don't encourage the model to predict something overconfident: in our Imagenette example where we have 10 classes, the targets become something like:\n",
+ "```\n",
+ "[0.01, 0.01, 0.01, 0.91, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n",
+ "```\n",
+ "(here for a target that corresponds to the index 3). In practice, we don't want to one-hot encode the labels, and fortunately we won't need too (the one-hot encoding is just good to explain what label smoothing is and visualize it)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: Label smoothing, the paper"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is how the reasoning behind label smoothing was explained in the paper:\n",
+ "\n",
+ "\"This maximum is not achievable for finite $z_k$ but is approached if $z_y\\gg z_k$ for all $k\\neq y$ -- that is, if the logit corresponding to the ground-truth label is much great than all other logits. This, however, can cause two problems. First, it may result in over-fitting: if the model learns to assign full probability to the ground-truth label for each training example, it is not guaranteed to generalize. Second, it encourages the differences between the largest logit and all others to become large, and this, combined with the bounded gradient $\\frac{\\partial\\ell}{\\partial z_k}$, reduces the ability of the model to adapt. Intuitively, this happens because the model becomes too confident about its predictions.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's practice our paper reading skills to try to interpret this. \"This maximum\" is refering to the previous section of the paper, which talked about the fact that `1` is the value of the label for the positive class. So any value (except infinity) can't result in `1` after sigmoid or softmax. In a paper, you won't normally see \"any value\" written, but instead it would get a symbol; in this case, it's $z_k$. This is helpful in a paper, because it can be refered to again later, and the reader knows what value is being discussed.\n",
+ "\n",
+ "The it says: $z_y\\gg z_k$ for all $k\\neq y$. In this case, the paper immediately follows with \"that is...\", which is handy, because you can just read the English instead of the math. In the math, the $y$ is refering to the target ($y$ is defined earlier in the paper; sometimes it's hard to find where symbols are defined, but nearly all papers will define all their symbols somewhere), and $z_y$ is the activation corresponding to the target. So to get close to `1`, this activation needs to be much higher than all the others for that prediction.\n",
+ "\n",
+ "Next up is \"if the model learns to assign full probability to the ground-truth label for each training example, it is not guaranteed to generalize\". This is saying that making $z_y$ really big means we'll need large weights and large activations throughout our model. Large weights lead to \"bumpy\" functions, where a small change in input results in a big change to predictions. This is really bad for generalization, because it means just one pixel changing a bit could change our prediction entirely!\n",
+ "\n",
+ "Finally, we have \"it encourages the differences between the largest logit and all others to become large, and this, combined with the bounded gradient $\\frac{\\partial\\ell}{\\partial z_k}$, reduces the ability of the model to adapt\". The gradient of cross entropy, remember, is basically `output-target`, and both `output` and `target` are between zero and one. So the difference is between `-1` and `1`, which is why the paper says the gradient is \"bounded\" (it can't be infinite). Therefore our SGD steps are bounded too. \"Reduces the ability of the model to adapt\" means that it is hard for it to be updated in a transfer learning setting. This follows because the difference in loss due to incorrect predictions is unbounded, but we can only take a limited step each time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To use it in practice, we just have to change the loss function in our call to `Learner`:\n",
+ "\n",
+ "```python\n",
+ "model = xresnet50()\n",
+ "learn = Learner(dls, model, loss_func=LabelSmoothingCrossEntropy(), metrics=accuracy)\n",
+ "learn.fit_one_cycle(5, 3e-3)\n",
+ "```\n",
+ "\n",
+ "Like Mixup, you won't generally see significant improvements from label smoothing until you train more epochs. Try it yourself and see: how many epochs do you have to train before label smoothing shows an improvement?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusion"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You have now seen everything you need to train a state-of-the-art model in computer vision, whether from scratch or using tranfer learning. Now all you have to do is experiment on your own problems! See if training longer with Mixup and/or label smoothing avoids overfitting and gives you better results. Try progressive resizing, and test time augmentation.\n",
+ "\n",
+ "Most importantly, remember that if your dataset is big, there is no point prototyping on the whole thing. Find a small subset that is representative of the whole, like we did with Imagenette, and experiment on it.\n",
+ "\n",
+ "In the next three chapters, we will look at the other applications directly supported by fastai: collaborative filtering, tabular and text. We will go back to computer vision in the next section of the book, with a deep dive in convolutional neural networks in <>. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What is the difference between ImageNet and Imagenette? When is it better to experiment on one versus the other?\n",
+ "1. What is normalization?\n",
+ "1. Why didn't we have to care about normalization when using a pretrained model?\n",
+ "1. What is progressive resizing?\n",
+ "1. Implement progressive resizing in your own project. Did it help?\n",
+ "1. What is test time augmentation? How do you use it in fastai?\n",
+ "1. Is using TTA at inference slower or faster than regular inference? Why?\n",
+ "1. What is Mixup? How do you use it in fastai?\n",
+ "1. Why does Mixup prevent the model from being too confident?\n",
+ "1. Why does a training with Mixup for 5 epochs end up worse than a training without Mixup?\n",
+ "1. What is the idea behind label smoothing?\n",
+ "1. What problems in your data can label smoothing help with?\n",
+ "1. When using label smoothing with 5 categories, what is the target associated with the index 1?\n",
+ "1. What is the first step to take when you want to prototype quick experiments on a new dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research\n",
+ "\n",
+ "1. Use the fastai documentation to build a function that crops an image to a square in the four corners, then implement a TTA method that averages the predictions on a center crop and those four crops. Did it help? Is it better than the TTA method of fastai?\n",
+ "1. Find the Mixup paper on arxiv and read it. Pick one or two more recent articles introducing variants of Mixup and read them, then try to implement them on your problem.\n",
+ "1. Find the script training Imagenette using Mixup and use it as an example to build a script for a long training on your own project. Execute it and see if it helped.\n",
+ "1. Read the sidebar on the math of label smoothing, and look at the relevant section of the original paper, and see if you can follow it. Don't be afraid to ask for help!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/08_collab.ipynb b/08_collab.ipynb
new file mode 100644
index 000000000..76e0f8fc7
--- /dev/null
+++ b/08_collab.ipynb
@@ -0,0 +1,2259 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_collab]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Collaborative filtering deep dive"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introduction to collaborative filtering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One very common problem to solve is when you have a number of users, and a number of products, you then want to recommend which products are most likely to be useful for which users. There are many variations of this, for example, recommending movies (such as on Netflix), figuring out what to highlight for a user on a homepage, deciding what stories to show in a social media feed, and so forth. There is a general solution to this problem, called *collaborative filtering*, which works like this: have a look at what products the current user has used or liked, find other users that have used or liked similar products, and then recommend the products that those other users have used or liked.\n",
+ "\n",
+ "For example, on Netflix you may have watched lots of movies that are science-fiction, full of action, and were made in the 1970s. Netflix may not know these particular properties of the films you have watched, but it would be able to see that other people that have watched the same movies that you watched also tended to watch other movies that are science-fiction, full of action, and were made in the 1970s. In other words, to use this approach we don't necessarily need to know anything about the movies, except who like to watch them.\n",
+ "\n",
+ "There is actually a more general class of problems that this approach can solve; not necessarily just things involving users and products. Indeed, for collaborative filtering we more commonly refer to *items*, rather than *products*. Items could be links that you click on, diagnoses that are selected for patients, and so forth.\n",
+ "\n",
+ "The key foundational idea is that of *latent factors*. In the above Netflix example, we started with the assumption that you like old action sci-fi movies. But you never actually told Netflix that you like these kinds of movies. And Netflix never actually needed to add columns to their movies table saying which movies are of these types. But there must be some underlying concept of sci-fi, action, and movie age. And these concepts must be relevant for at least some people's movie watching decisions.\n",
+ "\n",
+ "For this chapter we are going to work on this movie review problem. We do not have access to Netflix's entire dataset of movie watching history, but there is a great dataset that we can use, called MovieLens. This dataset contains tens of millions of movie rankings (that is a combination of a movie ID, a user ID, and a numeric rating), although we will just use a subset of 100,000 of them for our example. If you're interested, it would be a great learning project to try and replicate this approach on the full 25 million recommendation dataset you can get from their website."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The dataset is available through the usual fastai function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.collab import *\n",
+ "from fastai2.tabular.all import *\n",
+ "path = untar_data(URLs.ML_100k)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "According to the `README`, the main table is in the file `u.data`. It is tab-separated and the columns are respectively user, movie, rating and timestamp. Since those names are not encoded, we need to indicate them when reading the file with pandas. Here is a way to open this table and take a look:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![\"Crosstab](\"images/att_00040.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have selected just a few of the most popular movies, and users who watch the most movies, for this crosstab example. The empty cells in this table are the things that we would like our model to learn to fill in. Those other places where a user has not reviewed the movie yet, presumably because they have not watched. So for each user, we would like to figure out which of those movies they might be most likely to enjoy.\n",
+ "\n",
+ "If we knew for each user to what degree they liked each important category that a movie might fall into, such as genre, age, preferred directors and actors, and so forth, and we knew the same information about each movie, then a simple way to fill in this table would be to multiply this information together for each movie and use a combination. For instance, assuming these factors range between -1 and positive one, and positive means high match and negative means low match, and the categories are science-fiction, action, and old movies, then we could represent the movie The Last Skywalker as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "last_skywalker = np.array([0.98,0.9,-0.9])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, for instance, we are scoring *very science-fiction* as 0.98, and *very not old* as -0.9. We could represent a user who likes modern sci-fi action movies as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "user1 = np.array([0.9,0.8,-0.6])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "…and we can now calculate the match between this combination:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.1420000000000003"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(user1*last_skywalker).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we multiply two vectors together and add up the results, this is known as the *dot product*. It is used a lot in machine learning, and forms the basis of matrix modification. We will be looking a lot more at matrix modification and dot products in <>."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: dot product: the mathematical operation of multiplying the elements of two vectors together, and then summing up the result."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "On the other hand, we might represent the movie Casablanca as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "casablanca = np.array([-0.99,-0.3,0.8])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "…and the match between this combination is:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-1.611"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(user1*casablanca).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Learning the latent factors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since we don't know what the latent factors actually are, and we don't know how to score them for each user and movie, we will learn them. There is surprisingly little distance from specifying the structure of a model, as we did in the last section, and learning one, since we can just use our general gradient descent approach.\n",
+ "\n",
+ "Step one of this approach was to randomly initialise some parameters. These parameters will be a set of latent factors for each user and movie. We will have to decide how many to use. We will discuss how to select this shortly, but for illustrative purposes let's use 5 for now. Because each user will have a set of these factors, and each movie will have a set of these factors, we can show these randomly initialise values right next to the users and movies in our crosstab, and we can then fill in the dot products for each of these combinations in the middle. For example, here's what it looks like in Microsoft Excel, with the top-left cell formula displayed as an example:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Latent](\"images/att_00041.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Step two of this approach was to calculate our predictions. As we've discussed, we can do this by simply taking the dot product of each movie with each user. If for instance the first latent user factor represents how much they like action movies, and the first latent movie factor represents if the movie has a lot of action or not, when the product of those will be particularly high if either the user likes action movie and the movie has a lot of action in it or if the user doesn't like action movie and the movie doesn't have any action in it. On the other hand, if we have a mismatch (a user loves action movies but the movie isn't, or the user doesn't like action movies and it is one), the product will be very low.\n",
+ "\n",
+ "Step three was to calculate our loss. We can use any loss function that we wish; that's pick mean squared error for now, since that is one reasonable way to represent the accuracy of a prediction.\n",
+ "\n",
+ "That's all we need. With this in place, we can optimise our parameters (that is, the latent factors) using stochastic gradient descent, such as to minimise the loss. At each step, the stochastic gradient descent optimiser will calculate the match between each movie and each user using the dot product, and will compare it to the actual rating that each user gave to each movie, and it will then calculate the derivative of this value, and will step the weights by multiplying this by the learning rate. After doing this lots of times, the loss will get better and better, and the recommendations will also get better and better."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Creating the DataLoaders"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We would rather see movie titles than their ids. The table `u.item` contains the coorespondance id to title:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls = CollabDataLoaders.from_df(ratings, item_name='title', bs=64)\n",
+ "dls.show_batch()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to represent collaborative filtering in PyTorch we can't just use the crosstab representation directly, especially if we want it to fit into our deep learning framework. We can represent our movie and user latent factor tables as simple matrices:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n_users = len(dls.classes['user'])\n",
+ "n_movies = len(dls.classes['title'])\n",
+ "n_factors = 5\n",
+ "\n",
+ "user_factors = torch.randn(n_users, n_factors)\n",
+ "movie_factors = torch.randn(n_movies, n_factors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To calculate the result for a particular movie and use a combination we have two look up the index of the movie in our movie latent factors matrix, and the index of the user in our user latent factors matrix, and then we can do our dot product between the two latent factor vectors. But *look up in an index* is not an operation which our deep learning models know how to do. They know how to do matrix products, and activation functions.\n",
+ "\n",
+ "It turns out that we can represent *look up in an index* as a matrix product! The trick is to replace our indices with one hot encoded vectors. He is an example of what happens if we multiply a vector by a one hot encoded vector representing the index three:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([-0.4586, -0.9915, -0.4052, -0.3621, -0.5908])"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "one_hot_3 = one_hot(3, n_users).float()\n",
+ "user_factors.t() @ one_hot_3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It gives us the same vector as the one at index 3 in the matrix:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([-0.4586, -0.9915, -0.4052, -0.3621, -0.5908])"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "user_factors[3]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we do that for a few indices at once, we will have a matrix of one-hot encoded vectors and that operation will be a matrix multiplication! This would be a perfectly acceptable way to build models using this kind of architecture, except that it would use a lot more memory and time than necessary. We know that there is no real underlying reason to store the one hot encoded vector, or to search through it to find the occurrence of the number one — we should just be able to index into an array directly with an integer. Therefore, most deep learning libraries, including PyTorch, include a special layer which does just this; it indexes into a vector using an integer, but has its derivative calculated in such a way that it is identical to what it would have been if it had of done a matrix multiplication with a one hot encoded vector. This is called an *embedding*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: embedding layer: multiplying by a one hot encoded matrix, using the computational shortcut that it can be implemented by simply indexing directly. It is quite a fancy word for a very simple concept. The thing that you multiply the one hot encoded matrix by (or, using the computational shortcut, index into directly) is called the _embedding matrix_."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In computer vision, we had a very easy way to get all the information of a pixel through its RGB values. Those three numbers gave us the red-ness, the green-ness and the blue-ness, which was enough to get our model to work afterward.\n",
+ "\n",
+ "For the problem at hand, we don't have the same easy way to characterize a user or a movie. There is probably relations with genres: if a given user likes romance, he is likely to put higher scores to romance movie. Or wether the movie is more action-centered vs heavy on dialogue. Or the presence of a specific actor that one use might particularly like. \n",
+ "\n",
+ "How do we determine numbers to characterize those? The answer is, we don't. We will let our model *learn* them. By analyzing the existing relations between users and movies, let our model figure out itself the features that seem important or not.\n",
+ "\n",
+ "This is what embeddings are. We will attribute to each of our users and each of our movie a random vector of a certain length (here `n_factors=5`), and we will make those learnable parameters. That means that at each step, when we compute the loss by comparing our predictions to our targets, we will compute the gradients of the loss with respect to those embedding vectors and udpate them with the rule of SGD (or another optimizer).\n",
+ "\n",
+ "At the beginning, those numbers don't mean anything since we have chosen them randomly, but by the end of training, they will. By learning on existing data between users and movies, without having any other information, we will see that they still get some important features, and can isolate blockbusters from indepent cinema, action movies from romance...\n",
+ "\n",
+ "We are now in a position that we can create our whole model from scratch."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Collaborative filtering from scratch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before we can write a model in PyTorch, we first need to learn the basics of object-oriented programming and Python. If you haven't done any object oriented programming before, we will give you a quick introduction here, but we would recommend looking up a tutorial and doing some practice before moving on.\n",
+ "\n",
+ "The key idea in object-oriented programming is the *class*. We have been using classes throughout this book, such as DataLoader, string, and Learner. Python makes it easy for us to create new classes. Here is an example of a simple class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Example:\n",
+ " def __init__(self, a): self.a = a\n",
+ " def say(self,x): return f'Hello {self.a}, {x}.'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The most important piece of this is the special method called `__init__` (pronounced *dunder init*). In Python, any method surrounded in double underscores like this is considered special. It indicates that there is some extra behaviour associated with this method name. In the case of `__init__`, this is the method which Python will call when your new object is created. So, this is where you can set up any state which needs to be done upon object creation. Any parameters included when the user constructs an instance of your class will be passed to the `__init__` method is parameters. Note that the first parameter to any methods defined inside a class is `self`, so you can use this to set and get any attributes that you will need."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Hello Sylvain, nice to meet you.'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ex = Example('Sylvain')\n",
+ "ex.say('nice to meet you')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Also note that creating a new PyTorch module requires inheriting from Module. *Inheritance* is an important object-oriented concept which we will not discuss in detail here — in short, it means that we can add additional behaviour to an existing class. PyTorch already provides a Module class, which provides some basic foundations that we want to build on. So, we add the name of this *super class* after the name of the class that we are defining, as you see above.\n",
+ "\n",
+ "The final thing that you need to know to create a new PyTorch module, is that when your module is called, PyTorch will call a method in your class called `forward`, and will pass along to that any parameters that are included in the call. Here is our dot product model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DotProduct(Module):\n",
+ " def __init__(self, n_users, n_movies, n_factors):\n",
+ " self.user_factors = Embedding(n_users, n_factors)\n",
+ " self.movie_factors = Embedding(n_movies, n_factors)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " users = self.user_factors(x[:,0])\n",
+ " movies = self.movie_factors(x[:,1])\n",
+ " return (users * movies).sum(dim=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you haven't seen object-oriented programming before, then don't worry, you won't need to use it much in this book. We are just mentioning this approach here, because most online tutorials and documentation will use the object-oriented syntax.\n",
+ "\n",
+ "Note that the input of the model is a tensor of shape `batch_size x 2`, where the first columns (`x[:, 0]`) contains the user ids and the second column (`x[:, 1]`) contains the movie ids. As explained before, we use the *embedding* layers to represent our matrices of user and movie latent factors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 2])"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x,y = dls.one_batch()\n",
+ "x.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have defined our architecture, and created our parameter matrices, we need to create a `Learner` to optimize our model. In the past we have used special functions, such as `cnn_learner`, which set up everything for us for a particular application. Since we are doing things from scratch here, we will use the plain `Learner` class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = DotProduct(n_users, n_movies, 50)\n",
+ "learn = Learner(dls, model, loss_func=MSELossFlat())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are now ready to fit our model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(5, 5e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first thing we can do to make this model a little bit better is to force those predictions between 0 and 5. For this, we just need to use `sigmoid_range`, like in the previous chapter. One thing we discovered empirically is that it's better to have the range go a little bit over 5, so we use `(0, 5.5)`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DotProduct(Module):\n",
+ " def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
+ " self.user_factors = Embedding(n_users, n_factors)\n",
+ " self.movie_factors = Embedding(n_movies, n_factors)\n",
+ " self.y_range = y_range\n",
+ " \n",
+ " def forward(self, x):\n",
+ " users = self.user_factors(x[:,0])\n",
+ " movies = self.movie_factors(x[:,1])\n",
+ " return sigmoid_range((users * movies).sum(dim=1), *self.y_range)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = DotProduct(n_users, n_movies, 50)\n",
+ "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
+ "learn.fit_one_cycle(5, 5e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is a reasonable start, but we can do better. One obvious missing piece is that some users are just more positive or negative in their recommendations and others, and some movies are just plain better or worse than others. But in our dot product representation we do not have any way to encode either of these things. If all you can say, for instance, about the movie is that it is very sci-fi, very action oriented, and very not old, then you don't really have any way to say most people like it. \n",
+ "\n",
+ "That's because at this point we only have weights; we do not have biases. If we have a single number for each user which we add to our scores, and ditto for each movie, then this will handle this missing piece very nicely. So first of all, let's adjust our model architecture:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DotProductBias(Module):\n",
+ " def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
+ " self.user_factors = Embedding(n_users, n_factors)\n",
+ " self.user_bias = Embedding(n_users, 1)\n",
+ " self.movie_factors = Embedding(n_movies, n_factors)\n",
+ " self.movie_bias = Embedding(n_movies, 1)\n",
+ " self.y_range = y_range\n",
+ " \n",
+ " def forward(self, x):\n",
+ " users = self.user_factors(x[:,0])\n",
+ " movies = self.movie_factors(x[:,1])\n",
+ " res = (users * movies).sum(dim=1, keepdim=True)\n",
+ " res += self.user_bias(x[:,0]) + self.movie_bias(x[:,1])\n",
+ " return sigmoid_range(res, *self.y_range)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's try training this and see how it goes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = DotProductBias(n_users, n_movies, 50)\n",
+ "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
+ "learn.fit_one_cycle(5, 5e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Instead of being better, it ends up being worse (at least at the end of training). Why is that? If we look at both trainings carefully, we can see the validation loss stopped improving in the middle and started to get worse. As we've seen, this is a clear indication of overfitting. In this case, there is no way to use data augmentation, so we will have to use another regularisation technique. One approach that can be helpful is *weight decay*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Weight decay"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Weight decay, or L2 regularization, consists in adding to your loss function the sum of all the weights squared. Why do that? Because when we compute the gradients, it will add a contribution to them that will encourage the weights to be as small as possible.\n",
+ "\n",
+ "Why would it prevent overfitting? The idea is that the larger the coefficient are, the more sharp canyons we will have in the loss function. If we take the basic example of parabola, `y = a * (x**2)`, the larger `a` is, the more *narrow* the parabola is."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " user movie rating timestamp\n",
+ "0 196 242 3 881250949\n",
+ "1 186 302 3 891717742\n",
+ "2 22 377 1 878887116\n",
+ "3 244 51 2 880606923\n",
+ "4 166 346 1 886397596"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
+ " names=['user','movie','rating','timestamp'])\n",
+ "ratings.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although this has all the information we need, it is not a particularly helpful way for humans to look at this data. Here is the same data cross tabulated into a human friendly table:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "| \n", + " | user | \n", + "movie | \n", + "rating | \n", + "timestamp | \n", + "
|---|---|---|---|---|
| 0 | \n", + "196 | \n", + "242 | \n", + "3 | \n", + "881250949 | \n", + "
| 1 | \n", + "186 | \n", + "302 | \n", + "3 | \n", + "891717742 | \n", + "
| 2 | \n", + "22 | \n", + "377 | \n", + "1 | \n", + "878887116 | \n", + "
| 3 | \n", + "244 | \n", + "51 | \n", + "2 | \n", + "880606923 | \n", + "
| 4 | \n", + "166 | \n", + "346 | \n", + "1 | \n", + "886397596 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have selected just a few of the most popular movies, and users who watch the most movies, for this crosstab example. The empty cells in this table are the things that we would like our model to learn to fill in. Those other places where a user has not reviewed the movie yet, presumably because they have not watched. So for each user, we would like to figure out which of those movies they might be most likely to enjoy.\n",
+ "\n",
+ "If we knew for each user to what degree they liked each important category that a movie might fall into, such as genre, age, preferred directors and actors, and so forth, and we knew the same information about each movie, then a simple way to fill in this table would be to multiply this information together for each movie and use a combination. For instance, assuming these factors range between -1 and positive one, and positive means high match and negative means low match, and the categories are science-fiction, action, and old movies, then we could represent the movie The Last Skywalker as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "last_skywalker = np.array([0.98,0.9,-0.9])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, for instance, we are scoring *very science-fiction* as 0.98, and *very not old* as -0.9. We could represent a user who likes modern sci-fi action movies as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "user1 = np.array([0.9,0.8,-0.6])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "…and we can now calculate the match between this combination:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.1420000000000003"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(user1*last_skywalker).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we multiply two vectors together and add up the results, this is known as the *dot product*. It is used a lot in machine learning, and forms the basis of matrix modification. We will be looking a lot more at matrix modification and dot products in <"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Step two of this approach was to calculate our predictions. As we've discussed, we can do this by simply taking the dot product of each movie with each user. If for instance the first latent user factor represents how much they like action movies, and the first latent movie factor represents if the movie has a lot of action or not, when the product of those will be particularly high if either the user likes action movie and the movie has a lot of action in it or if the user doesn't like action movie and the movie doesn't have any action in it. On the other hand, if we have a mismatch (a user loves action movies but the movie isn't, or the user doesn't like action movies and it is one), the product will be very low.\n",
+ "\n",
+ "Step three was to calculate our loss. We can use any loss function that we wish; that's pick mean squared error for now, since that is one reasonable way to represent the accuracy of a prediction.\n",
+ "\n",
+ "That's all we need. With this in place, we can optimise our parameters (that is, the latent factors) using stochastic gradient descent, such as to minimise the loss. At each step, the stochastic gradient descent optimiser will calculate the match between each movie and each user using the dot product, and will compare it to the actual rating that each user gave to each movie, and it will then calculate the derivative of this value, and will step the weights by multiplying this by the learning rate. After doing this lots of times, the loss will get better and better, and the recommendations will also get better and better."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Creating the DataLoaders"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We would rather see movie titles than their ids. The table `u.item` contains the coorespondance id to title:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " movie title\n",
+ "0 1 Toy Story (1995)\n",
+ "1 2 GoldenEye (1995)\n",
+ "2 3 Four Rooms (1995)\n",
+ "3 4 Get Shorty (1995)\n",
+ "4 5 Copycat (1995)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "movies = pd.read_csv(path/'u.item', delimiter='|', encoding='latin-1',\n",
+ " usecols=(0,1), names=('movie','title'), header=None)\n",
+ "movies.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next we merge it to our ratings to get the titles."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | movie | \n", + "title | \n", + "
|---|---|---|
| 0 | \n", + "1 | \n", + "Toy Story (1995) | \n", + "
| 1 | \n", + "2 | \n", + "GoldenEye (1995) | \n", + "
| 2 | \n", + "3 | \n", + "Four Rooms (1995) | \n", + "
| 3 | \n", + "4 | \n", + "Get Shorty (1995) | \n", + "
| 4 | \n", + "5 | \n", + "Copycat (1995) | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " user movie rating timestamp title\n",
+ "0 196 242 3 881250949 Kolya (1996)\n",
+ "1 63 242 3 875747190 Kolya (1996)\n",
+ "2 226 242 5 883888671 Kolya (1996)\n",
+ "3 154 242 3 879138235 Kolya (1996)\n",
+ "4 306 242 5 876503793 Kolya (1996)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ratings = ratings.merge(movies)\n",
+ "ratings.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can then build a `DataLoaders` object from this table. By default, it takes the first column for user, the second column for the item (here our movies) and the third column for the ratings. We need to change the value of `item_name` in our case, to use the titles instead of the ids:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | user | \n", + "movie | \n", + "rating | \n", + "timestamp | \n", + "title | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "196 | \n", + "242 | \n", + "3 | \n", + "881250949 | \n", + "Kolya (1996) | \n", + "
| 1 | \n", + "63 | \n", + "242 | \n", + "3 | \n", + "875747190 | \n", + "Kolya (1996) | \n", + "
| 2 | \n", + "226 | \n", + "242 | \n", + "5 | \n", + "883888671 | \n", + "Kolya (1996) | \n", + "
| 3 | \n", + "154 | \n", + "242 | \n", + "3 | \n", + "879138235 | \n", + "Kolya (1996) | \n", + "
| 4 | \n", + "306 | \n", + "242 | \n", + "5 | \n", + "876503793 | \n", + "Kolya (1996) | \n", + "
| \n", + " | user | \n", + "title | \n", + "rating | \n", + "
|---|---|---|---|
| 0 | \n", + "207 | \n", + "Four Weddings and a Funeral (1994) | \n", + "3 | \n", + "
| 1 | \n", + "565 | \n", + "Remains of the Day, The (1993) | \n", + "5 | \n", + "
| 2 | \n", + "506 | \n", + "Kids (1995) | \n", + "1 | \n", + "
| 3 | \n", + "845 | \n", + "Chasing Amy (1997) | \n", + "3 | \n", + "
| 4 | \n", + "798 | \n", + "Being Human (1993) | \n", + "2 | \n", + "
| 5 | \n", + "500 | \n", + "Down by Law (1986) | \n", + "4 | \n", + "
| 6 | \n", + "409 | \n", + "Much Ado About Nothing (1993) | \n", + "3 | \n", + "
| 7 | \n", + "721 | \n", + "Braveheart (1995) | \n", + "5 | \n", + "
| 8 | \n", + "316 | \n", + "Psycho (1960) | \n", + "2 | \n", + "
| 9 | \n", + "883 | \n", + "Judgment Night (1993) | \n", + "5 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "1.326261 | \n", + "1.295701 | \n", + "00:12 | \n", + "
| 1 | \n", + "1.091352 | \n", + "1.091475 | \n", + "00:11 | \n", + "
| 2 | \n", + "0.961574 | \n", + "0.977690 | \n", + "00:11 | \n", + "
| 3 | \n", + "0.829995 | \n", + "0.893122 | \n", + "00:11 | \n", + "
| 4 | \n", + "0.781661 | \n", + "0.876511 | \n", + "00:12 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.976380 | \n", + "1.001455 | \n", + "00:12 | \n", + "
| 1 | \n", + "0.875964 | \n", + "0.919960 | \n", + "00:12 | \n", + "
| 2 | \n", + "0.685377 | \n", + "0.870664 | \n", + "00:12 | \n", + "
| 3 | \n", + "0.483701 | \n", + "0.874071 | \n", + "00:12 | \n", + "
| 4 | \n", + "0.385249 | \n", + "0.878055 | \n", + "00:12 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.929161 | \n", + "0.936303 | \n", + "00:13 | \n", + "
| 1 | \n", + "0.820444 | \n", + "0.861306 | \n", + "00:13 | \n", + "
| 2 | \n", + "0.621612 | \n", + "0.865306 | \n", + "00:14 | \n", + "
| 3 | \n", + "0.404648 | \n", + "0.886448 | \n", + "00:13 | \n", + "
| 4 | \n", + "0.292948 | \n", + "0.892580 | \n", + "00:13 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "x = np.linspace(-2,2,100)\n",
+ "a_s = [1,2,5,10,50] \n",
+ "ys = [a * x**2 for a in a_s]\n",
+ "_,ax = plt.subplots(figsize=(8,6))\n",
+ "for a,y in zip(a_s,ys): ax.plot(x,y, label=f'a={a}')\n",
+ "ax.set_ylim([0,5])\n",
+ "ax.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So by letting our model learn high parameters, it might fit all the data points in the training set with an over-complex function that has very sharp changes, which will lead to overfitting.\n",
+ "\n",
+ "Limiting our weights from growing to much is going to hinder the training of the model, but it will yield to a state where it generalizes better. Going back to the theory a little bit, weight decay (or just `wd`) is a parameter that controls that sum of squares we add to our loss (assuming `parameters` is a tensor of all parameters):\n",
+ "\n",
+ "``` python\n",
+ "loss_with_wd = loss + wd * (parameters**2).sum()\n",
+ "```\n",
+ "\n",
+ "In practice though, it would be very inefficient (and maybe numerically unstable) to compute that big sum and add it to the loss. If you remember a little bit of high schoool math, you might recall that the derivative of `p**2` with respect to `p` is `2*p`, so adding that big sum to our loss is exactly the same as doing:\n",
+ "\n",
+ "``` python\n",
+ "weight.grad += wd * 2 * weight\n",
+ "```\n",
+ "\n",
+ "In practice, since `wd` is a parameter that we choose, we can just make it twice as bit, so we don't even need the `*2` in the above equation. To use weight decay in fastai, just pass `wd` in your call to fit:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = DotProductBias(n_users, n_movies, 50)\n",
+ "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
+ "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Much better!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating our own Embedding module"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far, we've used `Embedding` without thinking about how it really works. Let's recreate DotProductBias *without* using this class. We'll need a randomly initialized weight matrix for each of the embeddings. We have to be careful, however. Recall from <> that optimizers require that they can get all the parameters of a module from a module's `parameters()` method. However, this does not happen fully automatically. If we just add a tensor as an attribute to a `Module`, it will not be included in `parameters`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#0) []"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class T(Module):\n",
+ " def __init__(self): self.a = torch.ones(3)\n",
+ "\n",
+ "L(T().parameters())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To tell `Module` that we want to treat a tensor as parameters, we have to wrap it in the `nn.Parameter` class. This class doesn't actually add any functionality (other than automatically calling `requires_grad_()` for us). It's only used as a \"marker\" to show what to include in `parameters()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#1) [Parameter containing:\n",
+ "tensor([1., 1., 1.], requires_grad=True)]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class T(Module):\n",
+ " def __init__(self): self.a = nn.Parameter(torch.ones(3))\n",
+ "\n",
+ "L(T().parameters())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "All PyTorch modules use `nn.Parameter` for any trainable parameters, which is why we haven't needed to explicitly use this wrapper up until now:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#1) [Parameter containing:\n",
+ "tensor([[-0.9595],\n",
+ " [-0.8490],\n",
+ " [ 0.8159]], requires_grad=True)]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class T(Module):\n",
+ " def __init__(self): self.a = nn.Linear(1, 3, bias=False)\n",
+ "\n",
+ "t = T()\n",
+ "L(t.parameters())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.nn.parameter.Parameter"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(t.a.weight)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create a tensor as a parameter, with random initialization, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_params(size):\n",
+ " return nn.Parameter(torch.zeros(*size).normal_(0, 0.01))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's use this to create `DotProductBias` again, but without `Embedding`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DotProductBias(Module):\n",
+ " def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
+ " self.user_factors = create_params([n_users, n_factors])\n",
+ " self.user_bias = create_params([n_users])\n",
+ " self.movie_factors = create_params([n_movies, n_factors])\n",
+ " self.movie_bias = create_params([n_movies])\n",
+ " self.y_range = y_range\n",
+ " \n",
+ " def forward(self, x):\n",
+ " users = self.user_factors[x[:,0]]\n",
+ " movies = self.movie_factors[x[:,1]]\n",
+ " res = (users*movies).sum(dim=1)\n",
+ " res += self.user_bias[x[:,0]] + self.movie_bias[x[:,1]]\n",
+ " return sigmoid_range(res, *self.y_range)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's train it again to check it's around the same results we saw in the previous section:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = DotProductBias(n_users, n_movies, 50)\n",
+ "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
+ "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Interpreting embeddings and biases"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at what our model has learned. It is already useful, in that it can provide us with recommendations for movies for our users — but it is also interesting to see what parameters it has discovered. The easiest to interpret are the biases. Here are the movies with the lowest values in the bias vector:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Children of the Corn: The Gathering (1996)',\n",
+ " 'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
+ " 'Beautician and the Beast, The (1997)',\n",
+ " 'Crow: City of Angels, The (1996)',\n",
+ " 'Home Alone 3 (1997)']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "movie_bias = learn.model.movie_bias.weight.squeeze()\n",
+ "idxs = movie_bias.argsort()[:5]\n",
+ "[dls.classes['title'][i] for i in idxs]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Have a think about what this means. What this is saying is, that for these movies, even when a user is very well matched to its latent factors (which, as we will see in a moment, tend to represent things like level of action, age of movie, and so forth) they still generally don't like it. We could have simply sorted movies directly by the average rating, but looking at their learned bias tells us something much more interesting. It tells us not just whether a movie is of a kind that people tend not to enjoy watching, but that people type like watching it even if it is of a kind that they would otherwise enjoy! By the same token, here are the movies with the highest bias:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['L.A. Confidential (1997)',\n",
+ " 'Titanic (1997)',\n",
+ " 'Silence of the Lambs, The (1991)',\n",
+ " 'Shawshank Redemption, The (1994)',\n",
+ " 'Star Wars (1977)']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "idxs = movie_bias.argsort(descending=True)[:5]\n",
+ "[dls.classes['title'][i] for i in idxs]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, for instance, even if you don't normally enjoy detective movies, you might enjoy LA Confidential!\n",
+ "\n",
+ "It is not quite so easy to directly interpret the embedding matrices. There is just too many factors for a human to look at. But there is a technique which can pull out the most important underlying *directions* in such a matrix, called PCA. We will not be going into this in detail in this book, because it is not particularly important for you to understand to be a deep learning practitioner, but if you are interested then we suggest you check out the fast.ai course, Computational Linear Algebra for Coders. Here is what our movies look like based on two of the strongest PCA components:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.972090 | \n", + "0.962366 | \n", + "00:13 | \n", + "
| 1 | \n", + "0.875591 | \n", + "0.885106 | \n", + "00:13 | \n", + "
| 2 | \n", + "0.723798 | \n", + "0.839880 | \n", + "00:13 | \n", + "
| 3 | \n", + "0.586002 | \n", + "0.823225 | \n", + "00:13 | \n", + "
| 4 | \n", + "0.490980 | \n", + "0.823060 | \n", + "00:13 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.962146 | \n", + "0.936952 | \n", + "00:14 | \n", + "
| 1 | \n", + "0.858084 | \n", + "0.884951 | \n", + "00:14 | \n", + "
| 2 | \n", + "0.740883 | \n", + "0.838549 | \n", + "00:14 | \n", + "
| 3 | \n", + "0.592497 | \n", + "0.823599 | \n", + "00:14 | \n", + "
| 4 | \n", + "0.473570 | \n", + "0.824263 | \n", + "00:14 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "g = ratings.groupby('title')['rating'].count()\n",
+ "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
+ "top_idxs = tensor([learn.dls.classes['title'].o2i[m] for m in top_movies])\n",
+ "movie_w = learn.model.movie_factors.weight[top_idxs].cpu().detach()\n",
+ "movie_pca = movie_w.pca(3)\n",
+ "fac0,fac1,fac2 = movie_pca.t()\n",
+ "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
+ "idxs = list(range(50))\n",
+ "X = fac0[idxs]\n",
+ "Y = fac2[idxs]\n",
+ "plt.figure(figsize=(12,12))\n",
+ "plt.scatter(X, Y)\n",
+ "for i, x, y in zip(top_movies[idxs], X, Y):\n",
+ " plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see here that the model seems to have discovered a concept of *classic* versus *pop culture* movies, or perhaps it is *critically acclaimed* that is represented here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: no matter how many models I train, I never stop getting moved and surprised by how these randomly initialised bunches of numbers, trained with such simple mechanics, managed to discover things about my data all by themselves. It almost seems like cheating, that I can create code which does useful things, without ever actually telling it how to do those things!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using fastai.collab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "fastai can create and train a collaborative filtering model using the exact structure shown above by using `collab_learner`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The names of the layers can be seen by printing the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "EmbeddingDotBias(\n",
+ " (u_weight): Embedding(944, 50)\n",
+ " (i_weight): Embedding(1635, 50)\n",
+ " (u_bias): Embedding(944, 1)\n",
+ " (i_bias): Embedding(1635, 1)\n",
+ ")"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn.model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use these to replicate any of the analyses we did in the previous section, for instance:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Titanic (1997)',\n",
+ " \"Schindler's List (1993)\",\n",
+ " 'Shawshank Redemption, The (1994)',\n",
+ " 'L.A. Confidential (1997)',\n",
+ " 'Silence of the Lambs, The (1991)']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "movie_bias = learn.model.i_bias.weight.squeeze()\n",
+ "idxs = movie_bias.argsort(descending=True)[:5]\n",
+ "[dls.classes['title'][i] for i in idxs]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Embedding distance"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "An interesting thing we can do with these learned embeddings is to look at _distance_. On a two-dimensional map we can calculate the distance between two coordinates using the formula of Pythagoras: $\\sqrt{x^{2}+y^{2}}$ (assuming that X and Y are the distances between the coordinates on each axis). For a 50 dimensional embedding we can do exactly the same thing, except that we add up the squares of all 50 of the coordinate distances.\n",
+ "\n",
+ "If there were two movies that were nearly identical, then there embedding vectors would also have to be nearly identical, because the users that would like them would be nearly exactly the same. There is a more general idea here: movie similarity can be defined by the similarity of users that like those movies. And that directly means that the distance between two movies' embedding vectors can define that similarity. We can use this to find the most similar movie to *Silence of the Lambs*:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Dial M for Murder (1954)'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "movie_factors = learn.model.i_weight.weight\n",
+ "idx = dls.classes['title'].o2i['Silence of the Lambs, The (1991)']\n",
+ "distances = nn.CosineSimilarity(dim=1)(movie_factors, movie_factors[idx][None])\n",
+ "idx = distances.argsort(descending=True)[1]\n",
+ "dls.classes['title'][idx]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Boot strapping a collaborative filtering model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The biggest challenge with using collaborative filtering models in practice is the *bootstrapping problem*. The most extreme version of this problem is when you have no users, and therefore no history to learn from. What product do you recommend to your very first user?\n",
+ "\n",
+ "But even if you are a well-established company with a long history of user transactions, you still have the question: what do you do when a new user signs up? And indeed, what do you do when you add a new product to your portfolio? There is no magic solution to this problem, and really the solutions that we suggest are just variations of the form *use your common sense*. You can start your new users such that they have the mean of all of the embedding vectors of your other users — although this has the problem that that particular combination of latent factors may be not at all common (for instance the average for the science-fiction factor may be high, and the average for the action factor may be low, but it is not that common to find people who like science-fiction without action). Better would probably be to pick some particular user to represent *average taste*.\n",
+ "\n",
+ "Better still is to use a tabular model based on user meta data to construct your initial embedding vector. When a user signs up, think about what questions you could ask them which could help you to understand their tastes. Then you can create a model where the dependent variable is a user's embedding vector, and the independent variables are the results of the questions that you ask them, along with their signup meta data. We will learn in the next section how to create these kinds of tabular models. You may have noticed that when you sign up for services such as Pandora and Netflix that they tend to ask you a few questions about what genres of movie or music that you like; this is how they come up with your initial collaborative filtering recommendations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing to be careful of is that a small number of extremely enthusiastic users may end up effectively setting the recommendations for your whole user base. This is a very common problem, for instance, in movie recommendation systems. People that watch anime tend to watch a whole lot of it, and don't watch very much else, and spend a lot of time putting their ratings into websites. As a result, a lot of *best ever movies* lists tend to be heavily overrepresented with anime. In this particular case, it can be fairly obvious that you have a problem of representation bias, but if the bias is occurring in the latent factors then it may not be obvious at all.\n",
+ "\n",
+ "Such a problem can change the entire make up of your user base, and the behaviour of your system. This is particularly true because of positive feedback loops. If a small number of your users tend to set the direction of your recommendation system, then they are naturally going to end up attracting more people like them to your system. And that will, of course, amplify the original representation bias. This is a natural tendency to be amplified exponentially. You may have seen examples of company executives expressing surprise at how their online platforms rapidly deteriorate in such a way that they express values that are at odds with the values of the founders. In the presence of these kinds of feedback loops, it is easy to see how such a divergence can happen both quickly, and in a way that is hidden until it is too late.\n",
+ "\n",
+ "In a self-reinforcing system like this, we should probably expect these kinds of feedback loops to be the norm, not the exception. Therefore, you should assume that you will see them, plan for that, and identify upfront how you will deal with these issues. Try to think about all of the ways in which feedback loops may be represented in your system, and how you might be able to identify them in your data. In the end, this is coming back to our original advice about how to avoid disaster when rolling out any kind of machine learning system. It's all about ensuring that there are humans in the loop, that there is careful monitoring, and gradual and thoughtful rollout."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deep learning for collaborative filtering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our dot product model works quite well, and it is the basis of many successful real-world recommendation systems. This approach to collaborative filtering is known as *probabilistic matrix factorisation* (PMF). Another approach, which generally works similarly well given the same data, is deep learning.\n",
+ "\n",
+ "To turn our architecture into a deep learning model the first step is to take the results of the embedding look up, and concatenating those activations together. This gives us a matrix which we can then pass through linear layers and nonlinearities in the usual way.\n",
+ "\n",
+ "Since we'll be concatenating the embedding matrices, rather than taking their dot product, that means that the two embedding matrices can have different sizes (i.e. different numbers of latent factors). fastai has a function `get_emb_sz` that returns recommended sizes for embedding matrices for your data, based on a heuristic that fast.ai has found tends to work well in practice:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(944, 74), (1635, 101)]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "embs = get_emb_sz(dls)\n",
+ "embs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's implement this class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class CollabNN(Module):\n",
+ " def __init__(self, user_sz, item_sz, y_range=(0,5.5), n_act=100):\n",
+ " self.user_factors = Embedding(*user_sz)\n",
+ " self.item_factors = Embedding(*item_sz)\n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(user_sz[1]+item_sz[1], n_act),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(n_act, 1))\n",
+ " self.y_range = y_range\n",
+ " \n",
+ " def forward(self, x):\n",
+ " embs = self.user_factors(x[:,0]),self.item_factors(x[:,1])\n",
+ " x = self.layers(torch.cat(embs, dim=1))\n",
+ " return sigmoid_range(x, *self.y_range)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and use it to create a model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = CollabNN(*embs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`CollabNN` creates our `Embedding` layers in the same way as previous classes in this chapter, except that we now use the `embs` sizes. Then `self.layers` is identical to the mini neural net we created in <> for MNIST. Then, in `forward`, we apply the embeddings, concatenate the results, and pass it through the mini neural net. Finally, we apply `sigmoid_range` as we have in previous models.\n",
+ "\n",
+ "Let's see if it trains:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
+ "learn.fit_one_cycle(5, 5e-3, wd=0.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fastai provides this model in fastai.collab, if you pass `use_nn=True` in your call to `collab_learner` (including calling `get_emb_sz` for you), plus lets you easily create more layers. For instance, here we're creating two hidden layers, of size 100 and 50, respectively:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = collab_learner(dls, use_nn=True, y_range=(0, 5.5), layers=[100,50])\n",
+ "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`learn.model` is an object of type `EmbeddingNN`. Let's take a look at fastai's code for this class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@delegates(TabularModel)\n",
+ "class EmbeddingNN(TabularModel):\n",
+ " def __init__(self, emb_szs, layers, **kwargs):\n",
+ " super().__init__(emb_szs, layers=layers, n_cont=0, out_sz=1, **kwargs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Wow that's not a lot of code! This class *inherits* from `TabularModel`, which is where it gets all its functionality from. In `__init__` is calls the same method in `TabularModel`, passing `n_cont=0` and `out_sz=1`; other than that, it only passes along whatever arguments it received."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sidebar: kwargs and delegates"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`EmbeddingNN` includes `**kwargs` as a parameter to `__init__`. In python `**kwargs` in a parameter like means \"put any additional keyword arguments into a dict called `kwarg`. And `**kwargs` in an argument list means \"insert all key/value pairs in the `kwargs` dict as named arguments here\". This approach is used in many popular libraries, such as `matplotlib`, in which the main `plot` function simply has the signature `plot(*args, **kwargs)`. The [plot documentation](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) says \"*The `kwargs` are Line2D properties*\" and then lists those properties.\n",
+ "\n",
+ "We're using `**kwargs` in `EmbeddingNN` to avoid having to write all the arguments to `TabularModel` a second time, and keep them in sync. However, this makes our API quite difficult to work with, because now Jupyter Notebook doesn't know what parameters are available, so things like tab-completion of parameter names and popup lists of signatures won't work.\n",
+ "\n",
+ "Fastai resolves this by providing a special `@delegates` decorator, which automatically changes the signature of the class or function (`EmbeddingNN` in this case) to insert all of its keyword arguments into the signature"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### End sidebar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although the results of `EmbeddingNN` are a bit worse than the dot product approach (which shows the power of carefully using an architecture for a domain), it does allow us to do something very important: we can now directly incorporate other user and movie information, time, and other information that may be relevant to the recommendation. That's exactly what `TabularModel` does. In fact, we've now seen that `EmbeddingNN` is just a `TabularModel`, with `n_cont=0` and `out_sz=1`. So we better spend some time learning about `TabularModel`, and how to use it to get great results!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What problem does collaborative filtering solve?\n",
+ "1. How does it solve it?\n",
+ "1. Why might a collaborative filtering predictive model fail to be a very useful recommendation system?\n",
+ "1. What does a crosstab representation of collaborative filtering data look like?\n",
+ "1. Write the code to create a crosstab representation of the MovieLens data (you might need to do some web searching!)\n",
+ "1. What is a latent factor? Why is it \"latent\"?\n",
+ "1. What is a dot product? Calculate a dot product manually using pure python with lists.\n",
+ "1. What does `pandas.DataFrame.merge` do?\n",
+ "1. What is an embedding matrix?\n",
+ "1. What is the relationship between an embedding and a matrix of one-hot encoded vectors?\n",
+ "1. Why do we need `Embedding` if we could use one-hot encoded vectors for the same thing?\n",
+ "1. What does an embedding contain before we start training (assuming we're not using a prertained model)?\n",
+ "1. Create a class (without peeking, if possible!) and use it.\n",
+ "1. What does `x[:,0]` return?\n",
+ "1. Rewrite the `DotProduct` class (without peeking, if possible!) and train a model with it\n",
+ "1. What is a good loss function to use for MovieLens? Why? \n",
+ "1. What would happen if we used `CrossEntropy` loss with MovieLens? How would we need to change the model?\n",
+ "1. What is the use of bias in a dot product model?\n",
+ "1. What is another name for weight decay?\n",
+ "1. Write the equation for weight decay (without peeking!)\n",
+ "1. Write the equation for the gradient of weight decay. Why does it help reduce weights?\n",
+ "1. Why does reducing weights lead to better generalization?\n",
+ "1. What does `argsort` do in PyTorch?\n",
+ "1. Does sorting the movie biases give the same result as averaging overall movie ratings by movie? Why / why not?\n",
+ "1. How do you print the names and details of the layers in a model?\n",
+ "1. What is the \"bootstrapping problem\" in collaborative filtering?\n",
+ "1. How could you deal with the bootstrapping problem for new users? For new movies?\n",
+ "1. How can feedback loops impact collaborative filtering systems?\n",
+ "1. When using a neural network in collaborative filtering, why can we have different number of factors for movie and user?\n",
+ "1. Why is there a `nn.Sequential` in the `CollabNN` model?\n",
+ "1. What kind of model should be use if we want to add metadata about users and items, or information such as date and time, to a collaborative filter model?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research\n",
+ "\n",
+ "1. Take a look at all the differences between the `Embedding` version of `DotProductBias` and the `create_params` version, and try to understand why each of those changes is required. If you're not sure, try reverting each change, to see what happens. (NB: even the type of brackets used in `forward` has changed!)\n",
+ "1. Find three other areas where collaborative filtering is being used, and find out what pros and cons of this approach in those areas.\n",
+ "1. Complete this notebook using the full MovieLens dataset, and compare your results to online benchmarks. See if you can improve your accuracy. Look on the book website and forum for ideas. Note that there are more columns in the full dataset--see if you can use those too (the next chapter might give you ideas)\n",
+ "1. Create a model for MovieLens with works with CrossEntropy loss, and compare it to the model in this chapter."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": false,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/09_tabular.ipynb b/09_tabular.ipynb
new file mode 100644
index 000000000..f53d29127
--- /dev/null
+++ b/09_tabular.ipynb
@@ -0,0 +1,9638 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *\n",
+ "from kaggle import api\n",
+ "from pandas.api.types import is_string_dtype, is_numeric_dtype, is_categorical_dtype\n",
+ "from fastai2.tabular.all import *\n",
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "from sklearn.tree import DecisionTreeRegressor\n",
+ "from dtreeviz.trees import *\n",
+ "from IPython.display import Image, display_svg, SVG\n",
+ "\n",
+ "pd.options.display.max_rows = 20\n",
+ "pd.options.display.max_columns = 8"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_tabular]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Tabular modelling deep dive"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In tabular data some columns contain numerical data, like \"age\", others contain string values, like \"sex\". The numerical data can be directly fed to the model (with some optional preprocessing), but other columns need to be converted to numbers. Since the values in those correspond to different categories, we often call these type of variables *categorical variables*. The first type are called *continuous variables*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Continuous and categorical variables: \"Continuous variables\" are numerical data, such as \"age\" can be directly fed to the model, since you can add and multiply them directly. \"Categorical variables\" contain a number of discrete levels, such as \"movie id\", for which addition and multiplication don't have meaning (even if they're stored as numbers)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Categorical embeddings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At the end of 2015 the [Rossmann sales competition](https://www.kaggle.com/c/rossmann-store-sales) ran on Kaggle. Competitors were given a wide range of information about various stores in Germany, and were tasked with trying to predict their sales on a number of day. The goal was to help them to manage stock properly and to be able to properly satisfy the demand without holding unnecessary inventory. The official training set provided a lot of information about the stores. It was also permitted for competitors to use additional data, as long as that data was made public and available to all participants.\n",
+ "\n",
+ "One of the gold medalists used deep learning, in one of the earliest known examples of a state of the art deep learning tabular model. Their method involved far less feature engineering based on domain knowledge than the other gold medalists. They wrote a paper, [Entity Embeddings of Categorical Variables](https://arxiv.org/abs/1604.06737), about their approach. In an online-only chapter on the book website we show how to replicate their approach from scratch and attain the same accuracy shown in the paper. In the abstract of the paper they say:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> : Entity embedding not only reduces memory usage and speeds up neural networks compared with one-hot encoding, but more importantly by mapping similar values close to each other in the embedding space it reveals the intrinsic properties of the categorical variables... it is especially useful for datasets with lots of high cardinality features, where other methods tend to overfit... As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have already noticed all of these points when we built our collaborative filtering model. We can clearly see that these insights go far beyond just collaborative filtering, however.\n",
+ "\n",
+ "The paper also points out that (as we discussed in the last chapter) an embedding layer is exactly equivalent to placing an ordinary linear layer after every one-hot encoded input layer. They used the following diagram to show this equivalence. Note that \"dense layer\" is another term with the same meaning as \"linear layer\", the one-hot encoding layers represent inputs."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Entity](\"images/att_00018.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The insight is important because we already know how to train linear layers, so this shows that from the point of view of the architecture and our training algorithm the embedding layer is just another layer. We also saw this in practice in the last chapter, when we built a collaborative filtering neural network that looks exactly like this diagram.\n",
+ "\n",
+ "Where we analyzed the embedding weights for movie reviews, the authors of the entity embeddings paper analyzed the embedding weights for their sales prediction model. What they found was quite amazing, and illustrates their second key insight. This is that the embedding makes the categorical variables into something which is both continuous and also meaningful.\n",
+ "\n",
+ "The images below illustrate these ideas. They are based on the approaches used in the paper, along with some analysis we have added.\n",
+ "\n",
+ "On the left in the image below is a plot of the embedding matrix for the possible values of the `State` category. For a categorical variable we call the possible values of the variable its \"levels\" (or \"categories\" or \"classes\"), so here one level is \"Berlin,\" another is \"Hamburg,\" etc.. On the right is a map of Germany. The actual physical locations of the German states were not part of the provided data; yet, the model itself learned where they must be, based only on the behavior of store sales!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"State](\"images/att_00015.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do you remember how we talked about *distance* between embeddings? The authors of the paper plotted the distance between embeddings between stores against the actual geographic distance between the stores in practice. They found that they matched very closely!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Store](\"images/att_00016.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've even tried plotted the embeddings for days of the week and months of the year, and found that days and months that are near each other on the calendar ended up close as embeddings too."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Date](\"images/att_00017.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What stands out in these two examples is that we provide the model fundamentally categorical data about discrete entities (German states or days of the week), and then the model learns an embedding for these entities which defines a continuous notion of distance between them. Because the embedding distance was learned based on real patterns in the data, that distance tends to match up with our intuitions.\n",
+ "\n",
+ "In addition, it is also valuable in its own right that embeddings are continuous. It is valuable because models are better at understanding continuous variables. This is unsurprising considering models are built of many continous parameter weights, continuous activation values, all updated via gradient descent, a learning algorithm for finding the minimums of continuous functions.\n",
+ "\n",
+ "Is is also valuable because we can combine our continuous embedding values with truly continuous input data in a straightforward manner: we just concatenate the variables, and feed the concatenation into our first dense layer. In other words, the raw categorical data is transformed by an embedding layer, before it interacts with the raw continuous input data. This is how fastai, and the entity embeddings paper, handle tabular models containing continuous and categorical variables.\n",
+ "\n",
+ "This concatenation approach is, for instance, how Google do their recommendations on Google Play, as they explained in their paper [Wide & Deep Learning for Recommender Systems](https://arxiv.org/abs/1606.07792), and as shown in this figure from their paper:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"The](\"images/att_00019.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Interestingly, Google are actually combining both the two approaches we saw in the previous chapter: the *dot product* (which Google call *Cross Product*) and neural network approach."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Beyond deep learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But let's pause for a moment. So far, the solution to all of our modelling problems has been: *train a deep learning model*. And indeed, that is a pretty good rule of thumb for complex unstructured data like images, sounds, natural language text, and so forth. Deep learning also works very well for collaborative filtering. But it is not always the best starting point for analysing tabular data.\n",
+ "\n",
+ "Most machine learning courses will throw dozens of different algorithms at you, with a brief technical description of the math behind them and maybe a toy example. You're left confused by the enormous range of techniques shown and have little practical understanding of how to apply them.\n",
+ "\n",
+ "The good news is that modern machine learning can be distilled down to a couple of key techniques that are of very wide applicability. Recent studies have shown that the vast majority of datasets can be best modeled with just two methods:\n",
+ "\n",
+ "1. Ensembles of decision trees (i.e. Random Forests and Gradient Boosting Machines), mainly for structured data (such as you might find in a database table at most companies)\n",
+ "1. Multi-layered neural networks learnt with SGD (i.e. shallow and/or deep learning), mainly for unstructured data (such as audio, vision, and natural language)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although deep learning is nearly always clearly superior for unstructured data, these two approaches tend to give quite similar results for many kinds of structured data. But ensembles of decision trees tend to train faster, are often easier to interpret, do not require special GPU hardware for inference at scale, and often require less hyperparameter tuning. They have been popular for quite a lot longer than deep learning, so there is a more mature ecosystem for tooling and documentation around them.\n",
+ "\n",
+ "Most importantly, the critical step of intepreting a model of tabular data is significantly easier for decision tree ensembles. There are tools and methods for answering the pertinent questions. For instance, which columns in the dataset were the most important for your predictions? How are they related to the dependent variable? How do they interact with each other? And which particular features were most important for some particular observation?\n",
+ "\n",
+ "Therefore, ensembles of decision trees are our first approach for analysing a new tabular dataset.\n",
+ "\n",
+ "The exception to this guideline is when the dataset meets one of these conditions:\n",
+ "\n",
+ "- There are some high cardinality categorical variables that are very important (\"cardinality\" refers to the number of discrete levels representing categories, so a high cardinality categorical variable is something like a ZIP Code, which can take on thousands of possible levels)\n",
+ "- There are some columns which contain data which would be best understood with a neural network, such as plaintext data.\n",
+ "\n",
+ "In practice, when we deal with datasets which meet these exceptional conditions, we would always try both decision tree ensembles and deep learning to see which works best. For instance, in our case of collaborative filtering you by definition have at least two high cardinality categorical variables, the users and the movies, so it is likely that deep learning would be a useful approach. But in practice things tend to be less cut and dried, and there will often be a mixture of high and low cardinality categorical variables and continuous variables.\n",
+ "\n",
+ "Either way, it's clear that we are going to need to add decision tree ensembles to our modelling toolbox!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Up to now we've used PyTorch and fastai for pretty much all of our heavy lifting. But these libraries are mainly designed for algorithms that do lots of matrix multiplication and derivatives (that is, stuff like deep learning!) Decision trees don't depend on these operations at all, so PyTorch isn't much use.\n",
+ "\n",
+ "Instead, we will be largely relying on a library called scikit-learn (also known as *sklearn*). Scikit-learn is a popular library for creating machine learning models, using approaches that are not covered by deep learning. In addition, we'll need to do some tabular data processing and querying, so we'll want to use the Pandas library. Finally, we'll also need numpy, since that's the main numeric programming library that both sklearn and Pandas rely on.\n",
+ "\n",
+ "We don't have time to do a deep dive on all these libraries in this book, so we'll just be touching on some of the main parts of each. For a far more in depth discussion, we strongly suggest Wes McKinney's [Python for Data Analysis, 2nd ed](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/ref=asap_bc?ie=UTF8). Wes is the creator of Pandas, so you can be sure that the information is accurate!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will be looking at the Blue Book for Bulldozers Kaggle Competition: \"The goal of the contest is to predict the sale price of a particular piece of heavy equipment at auction based on its usage, equipment type, and configuration. The data is sourced from auction result postings and includes information on usage and equipment configurations.\"\n",
+ "\n",
+ "This is a very common type of dataset and prediction problem, and similar to what you may see in your project or workplace."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Kaggle Competitions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Kaggle is an awesome resource for aspiring data scientists or anyone looking to improve their machine learning skills. There is nothing like getting hands-on practice and receiving real-time feedback to help you improve your skills.\n",
+ "\n",
+ "Kaggle provides:\n",
+ "\n",
+ "1. Interesting datasets\n",
+ "2. Feedback on how you're doing\n",
+ "3. A leader board to see what's good, what's possible, and what's state-of-art.\n",
+ "4. Blog posts by winning contestants sharing useful tips and techniques.\n",
+ "\n",
+ "Until now all our datasets have been available to download through fastai's integrated dataset system. However, the dataset we will be using in this chapter is only available from Kaggle. Therefore, you will need to sign up to Kaggle, then you need to go to the [page for the competition](https://www.kaggle.com/c/bluebook-for-bulldozers). On that page click on \"rules\", and then \"I understand and accept\". (Although the competition has finished, and you will not be entering it, you still have to agree to the rules to be allowed to download the data).\n",
+ "\n",
+ "The easiest way to download Kaggle datasets is to use the Kaggle API. You can install this using pip by running this in a notebook cell:\n",
+ "\n",
+ " !pip install kaggle\n",
+ "\n",
+ "You need an API key to use the Kaggle API; to get one, go to \"my account\" on the Kaggle website, and click \"create new API token\". This will save a file called `kaggle.json` to your PC. We need to create this on your GPU server. To do so, open the file you downloaded, copy the contents, and paste them inside `''` below, e.g.: `creds = '{\"username\":\"xxx\",\"key\":\"xxx\"}'`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "creds = ''"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...then execute this cell (only needs to be run once):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred_path = Path('~/.kaggle/kaggle.json').expanduser()\n",
+ "if not cred_path.exists():\n",
+ " cred_path.parent.mkdir(exist_ok=True)\n",
+ " cred_path.write(creds)\n",
+ " cred_path.chmod(0o600)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now you can download datasets from Kaggle! We'll pick a path to download the dataset to:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Path('/home/jhoward/.fastai/archive/bluebook')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path = URLs.path('bluebook')\n",
+ "path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and use the Kaggle API to download the dataset to that path, and extract it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#7) [Path('TrainAndValid.csv'),Path('Machine_Appendix.csv'),Path('random_forest_benchmark_test.csv'),Path('Test.csv'),Path('median_benchmark.csv'),Path('ValidSolution.csv'),Path('Valid.csv')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "if not path.exists():\n",
+ " path.mkdir()\n",
+ " api.competition_download_cli('bluebook-for-bulldozers', path=path)\n",
+ " file_extract(path/'bluebook-for-bulldozers.zip')\n",
+ "\n",
+ "path.ls(file_type='text')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Look at the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Kaggle provides info about some of the fields of our dataset; on the [Kaggle Data info](https://www.kaggle.com/c/bluebook-for-bulldozers/data) page they say that the key fields in `train.csv` are:\n",
+ "\n",
+ "- SalesID: the unique identifier of the sale\n",
+ "- MachineID: the unique identifier of a machine. A machine can be sold multiple times\n",
+ "- saleprice: what the machine sold for at auction (only provided in train.csv)\n",
+ "- saledate: the date of the sale\n",
+ "\n",
+ "In any sort of data science work, it's **important to look at your data directly** to make sure you understand the format, how it's stored, what type of values it holds, etc.. Even if you've read descriptions about your data, the actual data may not be what you expect. We'll start by reading the training set into a Pandas DataFrame; note that we have to tell Pandas which columns contain dates. Generally it's a good idea to also specify `low_memory=False` unless Pandas actually runs out of memory and returns an error. The `low_memory` parameter, which is `True` by default, tells Pandas to only look at a few rows of data at a time to figure out what type of data is in each column. This means that Pandas can actually end up using a different data type for different rows, which generally leads to data processing errors or model training problems later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(path/'TrainAndValid.csv', low_memory=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['SalesID', 'SalePrice', 'MachineID', 'ModelID', 'datasource',\n",
+ " 'auctioneerID', 'YearMade', 'MachineHoursCurrentMeter', 'UsageBand',\n",
+ " 'saledate', 'fiModelDesc', 'fiBaseModel', 'fiSecondaryDesc',\n",
+ " 'fiModelSeries', 'fiModelDescriptor', 'ProductSize',\n",
+ " 'fiProductClassDesc', 'state', 'ProductGroup', 'ProductGroupDesc',\n",
+ " 'Drive_System', 'Enclosure', 'Forks', 'Pad_Type', 'Ride_Control',\n",
+ " 'Stick', 'Transmission', 'Turbocharged', 'Blade_Extension',\n",
+ " 'Blade_Width', 'Enclosure_Type', 'Engine_Horsepower', 'Hydraulics',\n",
+ " 'Pushblock', 'Ripper', 'Scarifier', 'Tip_Control', 'Tire_Size',\n",
+ " 'Coupler', 'Coupler_System', 'Grouser_Tracks', 'Hydraulics_Flow',\n",
+ " 'Track_Type', 'Undercarriage_Pad_Width', 'Stick_Length', 'Thumb',\n",
+ " 'Pattern_Changer', 'Grouser_Type', 'Backhoe_Mounting', 'Blade_Type',\n",
+ " 'Travel_Controls', 'Differential_Type', 'Steering_Controls'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's a lot of columns for us to look at! Try looking through the dataset to get a sense of what kind of information is in each one. We'll shortly see how to \"zero in\" on the most interesting bits.\n",
+ "\n",
+ "At the point, a good next step is to handle *ordinal columns*. This refers to columns containing strings or similar, but where those strings have a natural ordering. For instance, here are the levels of `ProductSize`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([nan, 'Medium', 'Small', 'Large / Medium', 'Mini', 'Large', 'Compact'], dtype=object)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['ProductSize'].unique()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can tell Pandas about a suitable ordering of these levels like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sizes = 'Large','Large / Medium','Medium','Small','Mini','Compact'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['ProductSize'] = df['ProductSize'].astype('category')\n",
+ "df['ProductSize'].cat.set_categories(sizes, ordered=True, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's important to note what metric is being used for a project. Generally, selecting the metric is an important part of the project setup. In many cases, choosing a good metric will require more than just selecting a variable that already exists. It is more like a design process. You should think carefully about which metric, or set of metric, actually measures the notion of model quality which matters to you. If no variable represents that metric, you should say if you can build the metric from the variables which are available.\n",
+ "\n",
+ "However, in this case Kaggle tells us what metric to use: RMSLE (root mean squared log error) between the actual and predicted auction prices. Here we need do only a small amount of processing to use this: we take the log of the prices, so that m_rmse of that value will give us what we ultimately need."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dep_var = 'SalePrice'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[dep_var] = np.log(df[dep_var])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Decision trees"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Decision tree ensembles, as the name suggests, rely on decision trees. So let's start there! A decision tree asks a series of binary (that is, yes or no) questions about the data, and on that basis makes a prediction. For instance, the first question might be \"was the equipment manufactured before 1990?\" The second question will depend on the result of the first question (which is why this is a tree); for equipment manufactured for 1990 the second question might be \"was the auction after 2005?\" And so forth…\n",
+ "\n",
+ "This sequence of questions is now a procedure for taking any data item, whether an item from the training set or a new one, and assigning that item to a group. Namely, after asking and answering the questions, we can say the item belongs to the group of all the other training data items which yielded the same set of answers to the questions. But what good is this? the goal of our model is to predict values for items, not to assign them into groups from the training dataset. The value of this is that we can now assign a prediction value for each of these groups--for regression, we take the target mean of the items in the group.\n",
+ "\n",
+ "Let's consider how we find the right questions to ask. Of course, we wouldn't want to have to create all these questions ourselves — that's what computers are for! The basic steps to train a decision tree can be written down very easily:\n",
+ "\n",
+ "1. Loop through each column of the dataset in turn\n",
+ "1. For each column, loop through each possible level of that column in turn\n",
+ "1. Try splitting the data into two groups, based on whether they are greater than or less than that value (or if it is a categorical variable, based on whether they are equal to or not equal to that level of that categorical variable)\n",
+ "1. Find the average sale price for each of those two groups, and see how close that is to the actual sale price of each of the items of equipment in that group. That is, treat this as a very simple \"model\" where our predictions are simply the average sale price of the item's group\n",
+ "1. After looping through all of the columns and possible levels for each, pick the split point which gave the best predictions using our very simple model\n",
+ "1. We now have two different groups for our data, based on this selected split. Treat each of these as separate datasets, and find the best split for each, by going back to step one for each group\n",
+ "1. Continue this process recursively, and until you have reached some stopping criterion for each group — for instance, stop splitting a group further when it has only 20 items in it.\n",
+ "\n",
+ "Although this is an easy enough algorithm to implement yourself (and it is a good exercise to do so) we can save some time by using the implementation built into sklearn.\n",
+ "\n",
+ "But even before using sklearn, we have to prepare our data somewhat before we can use it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> A: Here's a productive question to ponder. If you consider that the procedure for defining decision tree essentially chooses _sequence of splitting questions about variables_, you might ask yourself, how do we know this procedure chooses the _correct sequence_? The rule is to choose the splitting question which produces the best split, and then to apply the same rule to groups that split produces, and so on (this is known in computer science as a \"greedy\" approach). Can you imagine a scenario in which asking a “less powerful” splitting question would enable a better split down the road (or should I say down the trunk!) and lead to a better result overall?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Handling dates"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first piece of data preparation we need to do is to enrich our representation of dates. The fundamental basis of the decision tree which we just described is bisection -- dividing up a group into two. We look at the ordinal variables and divide up the dataset based on whether the variable's value is greater (or lower) than a threshhold, and we look at the categorical variables and divided up the datset based on whether the variable's level is a particular level. So this algorithm has a way of dividing up the dataset based on both ordinal and categorical data.\n",
+ "\n",
+ "How does this apply to a common data type, the date? You might want to treat a date as an ordinal value, because it is meaningful to say that one date is greater than another. However, dates are a bit different from most ordinal values in that some dates are qualitatively different from others in a way that that is often relevant to the systems we are modelling.\n",
+ "\n",
+ "So in order to help the above algorithm handle dates intelligently, we'd like our model to know more than whether a date is more recent or less recent. We might want our model to make decisions based on that date's day of week, on whether a day is a holiday, on what month it is in, and so forth. To do this, we replace every date column with a set of date metadata columns, such as holiday, day of week, and month. These columns provide categorical data that we suspect will be useful.\n",
+ "\n",
+ "Fastai comes with a function that will do this for us — we just have to pass a column name which contains dates:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = add_datepart(df, 'saledate')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's do the same for the test set while we're there:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = pd.read_csv(path/'Test.csv', low_memory=False)\n",
+ "df_test = add_datepart(df_test, 'saledate')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that there are now lots of new columns in our DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'saleYear saleMonth saleWeek saleDay saleDayofweek saleDayofyear saleIs_month_end saleIs_month_start saleIs_quarter_end saleIs_quarter_start saleIs_year_end saleIs_year_start saleElapsed'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "' '.join(o for o in df.columns if o.startswith('sale'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Using TabularPandas and TabularProc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A second piece of preparatory processing is to be sure we can handle strings and missing data. Out of the box, sklearn cannot do either. Instead we will use fastai's class `TabularPandas`, which wraps a Pandas data frame and provides a few conveniences. To populate a `TabularPandas`, we will use two `TabularProc`s, `Categorify` and `FillMissing`. A `TabularProc` is like a regular `Transform`, except that:\n",
+ "\n",
+ "- It returns the exact same object that's passed to it, after modifying the object *in-place*, and\n",
+ "- It runs the transform once, when data is first passed in, rather than lazily as the data is access.\n",
+ "\n",
+ "`Categorify` is a `TabularProc` which replaces a column with a numeric categorical column. `FillMissing` is an `TabularProc` which replaces missing values with the median of the column, and creates a new boolean column that is set to True for any row where the value was missing. These two transforms are needed for nearly every tabular dataset you will use, so it's a good starting point for your data processing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "procs = [Categorify, FillMissing]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`TabularPandas` will also handle splitting into training vs validation datasets for us. \n",
+ "\n",
+ "We need to be very careful about our validation set here. In particular we want to design it so that it is like the *test set* which Kaggle will use to judge the contest.\n",
+ "\n",
+ "Recall the distinction between a validation set and a test set, as discussed in <>. A validation set is data which we hold back from training in order to ensure that the training process does not overfit on the training data. A test set is data which is held back even more deeply, from us ourselves, in order to ensure that *we* don't overfit on the validation data, as we explore various model architectures and hyperparameters.\n",
+ "\n",
+ "We don't get to see the test set. But we do want to define our validation data so that it has the same sort of relationship to the training data as the test set will have.\n",
+ "\n",
+ "In some cases, just randomly choosing a subset of your data points is will do that. This is not one of those cases, because it is a time series.\n",
+ "\n",
+ "If you look at the date range represented in the test set, you will discover that it covers a six-month period from May 2012, which is later in time than any date in the training set. This is a good design, because the competition sponsor will want to ensure that a model is able to predict the future. But it means that if we are going to have a useful validation set, we also want the validation set to be later in time. The Kaggle training data ends in April 2012. So we will define a narrower training dataset which consists only of the Kaggle training data from before November 2011, and we define a validation set which is from after November 2011.\n",
+ "\n",
+ "To do this we use `np.where`, a useful function which returns (as the first element of a tuple) the indices of all `True` values:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cond = (df.saleYear<2011) | (df.saleMonth<10)\n",
+ "train_idx = np.where( cond)[0]\n",
+ "valid_idx = np.where(~cond)[0]\n",
+ "\n",
+ "splits = (list(train_idx),list(valid_idx))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`TabularPandas` needs to be told which columns are continuous, and which are categorical. We can handle that automatically using the helper function `cont_cat_split`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cont,cat = cont_cat_split(df, 1, dep_var=dep_var)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "to = TabularPandas(df, procs, cat, cont, y_names=dep_var, splits=splits)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A `TabularPandas` behaves a lot like a fastai `Datasets` object, including `train` and `valid` attributes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(404710, 7988)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(to.train),len(to.valid)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that the data still is displayed as strings for categories..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "to.show(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TK too big to fit"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...but the underlying items are all numeric:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "draw_tree(m, xs, size=7, leaves_parallel=True, precision=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Understanding this picture is one of the best ways to understand decision trees. So we will start at the top, and explain each part step-by-step.\n",
+ "\n",
+ "The top node represents the *initial model* before any splits have been done, when all the data is in one group. This is the simplest possible model. It is the result of asking zero questions. It always predict the value to be the average value of the whole dataset. In this case, we can see it predicts a value of 10.10 for the logarithm of the sales price. It gives a mean squared error of 0.48. The square root of this is 0.69. Remember that unless you see *m_rmse* or a *root mean squared error* then the value you are looking at is before taking the square root, so it is just the average of the square of the differences. We can also see that there are 404,710 auction records in this group — that is the total size of our training set. The final piece of information shown here is the decision criterion for the very first split that was found, which is to split based on the `coupler_system` column.\n",
+ "\n",
+ "Moving down and too the left, this node shows us that there were 360,847 auction records for equipment where `coupler_system` was less than 0.5. The average value of our dependent variable in this group is 10.21. But moving down and to the right from the initial model would take us to the records where `coupler_system` was greater than 0.5.\n",
+ "\n",
+ "The bottom row contains our *leaf nodes*, the nodes with no answers coming out of them, because there are no more questiosn to be answered. At the far right of this row is the node for `coupler_system` greater than 0.5, and we can see that the average value is 9.21. So we can see the decision tree algorithm did find a single binary decision which separated high value from low value auction results. Looking only at `coupler_system` predicts if the average value of 9.21 vs 10.1. That's if we ask only one question.\n",
+ "\n",
+ "Returning back to the top node after the first decision point, we can see that a second binary decision split has been made, based on asking whether `YearMade` is less than or equal to 1991.5. For the group where this is true (remember, this is now following two binary decisions, both `coupler_system`, and `YearMade`) the average value is 9.97, and there are 155,724 auction records in this group. For the group of auctions where this decision is false, the average value is 10.4, and there are 205,123 records. So again, we can see that the decision tree algorithm has successfully further split our more expensive auction records into two groups which differ in value significantly."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**(TK AG: I think it would be useful here to have a figure which showed a circle or blob shape, which is carved by bisecting lines first into two groups, then into three, then four, as new bisections are introduced. This is a valuable intuition which we have not depicted.)**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can show the same information using Terence Parr's powerful [dtreeviz](https://explained.ai/decision-tree-viz/) library:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "samp_idx = np.random.permutation(len(y))[:500]\n",
+ "dtreeviz(m, xs.iloc[samp_idx], y.iloc[samp_idx], xs.columns, dep_var,\n",
+ " fontname='DejaVu Sans', scale=1.6, label_fontsize=10,\n",
+ " orientation='LR')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This shows a chart of the distribution of the data for each split point. We can clearly see that there's a problem with our `YearMade` data: there are bulldozers made in the year 1000, apparently! Presumably this is actually just a missing value code. In a decision tree, we can set any value that doesn't otherwise appear in the data as a missing value code. So for modelling purposes, '1000' is fine; but it makes visualization a bit hard to see, as shown above. So let's replace it with '1950':"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xs.loc[xs['YearMade']<1900, 'YearMade'] = 1950\n",
+ "valid_xs.loc[valid_xs['YearMade']<1900, 'YearMade'] = 1950"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After that change, the split is much clearer in the tree visualization, even although it doesn't actually change the result of the model in any significant way. This is a great example of how resilient decision trees are to data issues!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = DecisionTreeRegressor(max_leaf_nodes=4).fit(xs, y)\n",
+ "\n",
+ "dtreeviz(m, xs.iloc[samp_idx], y.iloc[samp_idx], xs.columns, dep_var,\n",
+ " fontname='DejaVu Sans', scale=1.6, label_fontsize=10,\n",
+ " orientation='LR')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's now have the decision tree algorithm build a bigger tree (i.e we are not listing any stopping criteria such as `max_leaf_nodes`):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m = DecisionTreeRegressor()\n",
+ "m.fit(xs, y);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll create a little function to check the root mean squared error (m_rmse) of our model, since that's how the competition was judged:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def r_mse(pred,y): return round(math.sqrt(((pred-y)**2).mean()), 6)\n",
+ "def m_rmse(m, xs, y): return r_mse(m.predict(xs), y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.0"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m_rmse(m, xs, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, our model is perfect, right? Not so fast... remember we really need to check the validation set, to ensure we're not overfitting:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.337727"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m_rmse(m, valid_xs, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Oops... it looks like we might be overfitting pretty badly. Here's why:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(340909, 404710)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m.get_n_leaves(), len(xs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've got nearly as many leaf nodes as data points! That seems a little... over-enthusiastic. Indeed, sklearn's default settings allow it to continue splitting nodes until there is only one item in a leaf node. Let's change the stopping rule to tell sklearn to ensure every leaf node has at least 25 auctions:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> A: Here's my intuition for an overfitting decision tree with more leaf nodes than data items: the childhood game of Twenty Questions. In that game, the chooser secretly imagines an object (like, \"our television set\"), and the guesser gets to pose twenty yes-or-no questions to try to guess the object (like \"is it bigger than a breadbox?\"). The guesser is not trying to predict a numerical value but just to identify a particular object out of the set of all imaginable objects. When your decision tree has more leafs than there are possible objects in your domain, then it is essentially a well-trained guesser. It has learned the sequence of questions needed to identify a particular data item in the training set, and it is \"predicting\" only by describing that item's value. This is a way of memorizing the training set, i.e., of overfitting."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.248562, 0.32368)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = DecisionTreeRegressor(min_samples_leaf=25)\n",
+ "m.fit(to.train.xs, to.train.y)\n",
+ "m_rmse(m, xs, y), m_rmse(m, valid_xs, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That looks much better. Let's check the number of leaves again:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "12397"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m.get_n_leaves()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Much more reasonable!\n",
+ "\n",
+ "Building a decision tree is a good way to create a model of our data. It is very flexible, since it can clearly handle nonlinear relationships and interactions between variables. But we can see there is a fundamental compromise between how well it generalises (which we can achieve by creating small trees) and how accurate it is on the training set (which we can achieve by using large trees).\n",
+ "\n",
+ "But, how do we get the best of both worlds?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Categorical variables"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing that may have struck you during this process is that we have not done anything special to handle categorical variables.\n",
+ "\n",
+ "This is unlike the situation with deep learning networks, where we one-hot encoded the variables and then fed them to an embedding layer. There, the embedding layer helped to discover the meaning of these variable levels, since each level of a categorical variable does not have a meaning on its own (unless we manually specified an ordering using pandas). So how can these untreated categorical variables do anything useful in a decision tree? For instance, how could something like a product code be used?\n",
+ "\n",
+ "The short answer is: it just works! Think about a situation where there is one product code that is far more expensive at auction than any other one. In that case, any binary split will result in that one product code being in some group, and that group will be more expensive than the other group. Therefore, our simple decision tree building algorithm will choose that split. Later during training, the algorithm will be able to further split the subgroup which now contains the expensive product code. Over time, the tree will home in on that one expensive product.\n",
+ "\n",
+ "It is also possible to use one hot encoding to replace a single categorical variable with multiple one hot encoded columns, where column represents a possible level of the variable. Pandas has a `get_dummies` method which does just that.\n",
+ "\n",
+ "However, there is not really any evidence that such an approach improves the end result. So, we generally avoid it where possible, because it does end up making your dataset harder to work with. In 2019 this issue was explored in the paper [Splitting on categorical predictors in random forests](https://peerj.com/articles/6339/), which said:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> : \"The standard approach for nominal predictors is to consider all (2^(k − 1) − 1) 2-partitions of the k predictor categories. However, this exponential relationship produces a large number of potential splits to be evaluated, increasing computational complexity and restricting the possible number of categories in most implementations. For binary classification and regression, it was shown that ordering the predictor categories in each split leads to exactly the same splits as the standard approach. This reduces computational complexity because only k − 1 splits have to be considered for a nominal predictor with k categories.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Random forests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Introduction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In 1994 Berkeley professor Leo Breiman, one year after his retirement, published a small technical report called *Bagging Predictors*, which turned out to be one of the most influential ideas in modern machine learning. The report began:\n",
+ "\n",
+ "> : \"Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions... The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests… show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.\"\n",
+ "\n",
+ "Here is the procedure that Breiman is proposing:\n",
+ "\n",
+ "1. Randomly choose a subset of the rows of your data (i.e., \"bootstrap replicates of your learning set\")\n",
+ "1. Train a model using this subset\n",
+ "1. Save that model, and then return to step one a few times\n",
+ "1. This will give you a number of trained models. To make a prediction, predict using all of the models, and then take the average of each of those model's predictions.\n",
+ "\n",
+ "This procedure is known as \"bagging\". It is based on a deep and important insight: although each of the models trained on a subset of data will make more errors than a model trained on the full dataset, those errors will not be correlated with each other. Different models will make different errors. The average of those errors, therefore, is: zero! So if we take the average of all of the models' predictions, then we should end up with a prediction which gets closer and closer to the correct answer, the more models we have. This is an extraordinary result — it means that we can improve the accuracy of nearly any kind of machine learning algorithm by training it multiple times, each time on a different random subset of data, and average its predictions.\n",
+ "\n",
+ "In 2001 Leo Breiman went on to demonstrate that this approach to building models, when applied to decision tree building algorithms, was particularly powerful. He went even further than just randomly choosing rows for each model's training, but also randomly selected from a subset of columns when choosing each split in each decision tree. He called this method the *random forest*. Today it is, perhaps, the most widely used and practically important machine learning method.\n",
+ "\n",
+ "In essence a random forest is a model that averages the predictions of large number of decision trees, which are generated by randomly varying various parameters that specify what data is used to train the tree and other tree parameters. \"Bagging\" is a particular approach to \"ensembling\", which refers to any approach that combines the results of multiple models together."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "# pip install --pre -f https://sklearn-nightly.scdn8.secure.raxcdn.com scikit-learn --U"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating a random forest"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create a random forest just like we created a decision tree. Except now, we are also specifying parameters which indicate how many trees should be in the forest, how we should subset the data items (the rows), and how we should subset the fields (the columns).\n",
+ "\n",
+ "In the function definition below, `n_estimators` defines the number of trees we want, and `max_samples` defines how many rows to sample for training each tree, and `max_features` defines how many columns to sample at each split point (where `0.5` means \"take half the total number of columns\"). We can also pass parameters for choosing when to stop splitting the tree nodes, effectively limiting the depth of tree, by including the same `min_samples_leaf` parameter we used in the last section. Finally, we pass `n_jobs=-1` to tell sklearn to use all our CPUs to build the trees in parallel. By creating a little function for this, we can more quickly try different variations in the rest of this chapter."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def rf(xs, y, n_estimators=40, max_samples=200_000,\n",
+ " max_features=0.5, min_samples_leaf=5, **kwargs):\n",
+ " return RandomForestRegressor(n_jobs=-1, n_estimators=n_estimators,\n",
+ " max_samples=max_samples, max_features=max_features,\n",
+ " min_samples_leaf=min_samples_leaf, oob_score=True).fit(xs, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m = rf(xs, y);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our validation RMSE is now much improved over our last result produced by the `DecisionTreeRegressor`, which made just one tree using all available data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.170896, 0.233502)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m_rmse(m, xs, y), m_rmse(m, valid_xs, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the most important properties of random forests is that they aren't very sensitive to the hyperparameter choices, such as `max_features`. You can set `n_estimators` to as high a number as you have time to train -- the more trees, the more accurate they will be. `max_samples` can often be left at its default, unless you have over 200,000 data points, in which case setting it to 200,000 will make it train faster, with little impact on accuracy. `max_features=0.5`, and `min_samples_leaf=4` both tend to work well, although sklearn's defaults work well too.\n",
+ "\n",
+ "The sklearn docs [show an example](http://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html) of different `max_features` choices, with increasing numbers of trees. In the plot, the blue plot line uses the fewest features and the green line uses the most, since it uses all the features. As you can see, the models with the lowest error result from using a subset of features but with a larger number of trees:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"sklearn](\"images/sklearn_features.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see the impact of `n_estimators`, let's get the predictions from each individual tree in our forest (these are in the `estimators_` attribute):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds = np.stack([t.predict(valid_xs) for t in m.estimators_])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, `preds.mean(0)` gives the same results as our random forest:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.233502"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "r_mse(preds.mean(0), valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> question: Why does this give the same result as our random forest?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's how RMSE improves as we add more and more trees. As you can see, the improvement levels off quite a bit after around 30 trees:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.931751 | \n", + "0.953806 | \n", + "00:13 | \n", + "
| 1 | \n", + "0.851826 | \n", + "0.878119 | \n", + "00:13 | \n", + "
| 2 | \n", + "0.715254 | \n", + "0.834711 | \n", + "00:13 | \n", + "
| 3 | \n", + "0.583173 | \n", + "0.821470 | \n", + "00:13 | \n", + "
| 4 | \n", + "0.496625 | \n", + "0.821688 | \n", + "00:13 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.940104 | \n", + "0.959786 | \n", + "00:15 | \n", + "
| 1 | \n", + "0.893943 | \n", + "0.905222 | \n", + "00:14 | \n", + "
| 2 | \n", + "0.865591 | \n", + "0.875238 | \n", + "00:14 | \n", + "
| 3 | \n", + "0.800177 | \n", + "0.867468 | \n", + "00:14 | \n", + "
| 4 | \n", + "0.760255 | \n", + "0.867455 | \n", + "00:14 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "1.002747 | \n", + "0.972392 | \n", + "00:16 | \n", + "
| 1 | \n", + "0.926903 | \n", + "0.922348 | \n", + "00:16 | \n", + "
| 2 | \n", + "0.877160 | \n", + "0.893401 | \n", + "00:16 | \n", + "
| 3 | \n", + "0.838334 | \n", + "0.865040 | \n", + "00:16 | \n", + "
| 4 | \n", + "0.781666 | \n", + "0.864936 | \n", + "00:16 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The insight is important because we already know how to train linear layers, so this shows that from the point of view of the architecture and our training algorithm the embedding layer is just another layer. We also saw this in practice in the last chapter, when we built a collaborative filtering neural network that looks exactly like this diagram.\n",
+ "\n",
+ "Where we analyzed the embedding weights for movie reviews, the authors of the entity embeddings paper analyzed the embedding weights for their sales prediction model. What they found was quite amazing, and illustrates their second key insight. This is that the embedding makes the categorical variables into something which is both continuous and also meaningful.\n",
+ "\n",
+ "The images below illustrate these ideas. They are based on the approaches used in the paper, along with some analysis we have added.\n",
+ "\n",
+ "On the left in the image below is a plot of the embedding matrix for the possible values of the `State` category. For a categorical variable we call the possible values of the variable its \"levels\" (or \"categories\" or \"classes\"), so here one level is \"Berlin,\" another is \"Hamburg,\" etc.. On the right is a map of Germany. The actual physical locations of the German states were not part of the provided data; yet, the model itself learned where they must be, based only on the behavior of store sales!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do you remember how we talked about *distance* between embeddings? The authors of the paper plotted the distance between embeddings between stores against the actual geographic distance between the stores in practice. They found that they matched very closely!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've even tried plotted the embeddings for days of the week and months of the year, and found that days and months that are near each other on the calendar ended up close as embeddings too."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What stands out in these two examples is that we provide the model fundamentally categorical data about discrete entities (German states or days of the week), and then the model learns an embedding for these entities which defines a continuous notion of distance between them. Because the embedding distance was learned based on real patterns in the data, that distance tends to match up with our intuitions.\n",
+ "\n",
+ "In addition, it is also valuable in its own right that embeddings are continuous. It is valuable because models are better at understanding continuous variables. This is unsurprising considering models are built of many continous parameter weights, continuous activation values, all updated via gradient descent, a learning algorithm for finding the minimums of continuous functions.\n",
+ "\n",
+ "Is is also valuable because we can combine our continuous embedding values with truly continuous input data in a straightforward manner: we just concatenate the variables, and feed the concatenation into our first dense layer. In other words, the raw categorical data is transformed by an embedding layer, before it interacts with the raw continuous input data. This is how fastai, and the entity embeddings paper, handle tabular models containing continuous and categorical variables.\n",
+ "\n",
+ "This concatenation approach is, for instance, how Google do their recommendations on Google Play, as they explained in their paper [Wide & Deep Learning for Recommender Systems](https://arxiv.org/abs/1606.07792), and as shown in this figure from their paper:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Interestingly, Google are actually combining both the two approaches we saw in the previous chapter: the *dot product* (which Google call *Cross Product*) and neural network approach."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Beyond deep learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But let's pause for a moment. So far, the solution to all of our modelling problems has been: *train a deep learning model*. And indeed, that is a pretty good rule of thumb for complex unstructured data like images, sounds, natural language text, and so forth. Deep learning also works very well for collaborative filtering. But it is not always the best starting point for analysing tabular data.\n",
+ "\n",
+ "Most machine learning courses will throw dozens of different algorithms at you, with a brief technical description of the math behind them and maybe a toy example. You're left confused by the enormous range of techniques shown and have little practical understanding of how to apply them.\n",
+ "\n",
+ "The good news is that modern machine learning can be distilled down to a couple of key techniques that are of very wide applicability. Recent studies have shown that the vast majority of datasets can be best modeled with just two methods:\n",
+ "\n",
+ "1. Ensembles of decision trees (i.e. Random Forests and Gradient Boosting Machines), mainly for structured data (such as you might find in a database table at most companies)\n",
+ "1. Multi-layered neural networks learnt with SGD (i.e. shallow and/or deep learning), mainly for unstructured data (such as audio, vision, and natural language)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although deep learning is nearly always clearly superior for unstructured data, these two approaches tend to give quite similar results for many kinds of structured data. But ensembles of decision trees tend to train faster, are often easier to interpret, do not require special GPU hardware for inference at scale, and often require less hyperparameter tuning. They have been popular for quite a lot longer than deep learning, so there is a more mature ecosystem for tooling and documentation around them.\n",
+ "\n",
+ "Most importantly, the critical step of intepreting a model of tabular data is significantly easier for decision tree ensembles. There are tools and methods for answering the pertinent questions. For instance, which columns in the dataset were the most important for your predictions? How are they related to the dependent variable? How do they interact with each other? And which particular features were most important for some particular observation?\n",
+ "\n",
+ "Therefore, ensembles of decision trees are our first approach for analysing a new tabular dataset.\n",
+ "\n",
+ "The exception to this guideline is when the dataset meets one of these conditions:\n",
+ "\n",
+ "- There are some high cardinality categorical variables that are very important (\"cardinality\" refers to the number of discrete levels representing categories, so a high cardinality categorical variable is something like a ZIP Code, which can take on thousands of possible levels)\n",
+ "- There are some columns which contain data which would be best understood with a neural network, such as plaintext data.\n",
+ "\n",
+ "In practice, when we deal with datasets which meet these exceptional conditions, we would always try both decision tree ensembles and deep learning to see which works best. For instance, in our case of collaborative filtering you by definition have at least two high cardinality categorical variables, the users and the movies, so it is likely that deep learning would be a useful approach. But in practice things tend to be less cut and dried, and there will often be a mixture of high and low cardinality categorical variables and continuous variables.\n",
+ "\n",
+ "Either way, it's clear that we are going to need to add decision tree ensembles to our modelling toolbox!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Up to now we've used PyTorch and fastai for pretty much all of our heavy lifting. But these libraries are mainly designed for algorithms that do lots of matrix multiplication and derivatives (that is, stuff like deep learning!) Decision trees don't depend on these operations at all, so PyTorch isn't much use.\n",
+ "\n",
+ "Instead, we will be largely relying on a library called scikit-learn (also known as *sklearn*). Scikit-learn is a popular library for creating machine learning models, using approaches that are not covered by deep learning. In addition, we'll need to do some tabular data processing and querying, so we'll want to use the Pandas library. Finally, we'll also need numpy, since that's the main numeric programming library that both sklearn and Pandas rely on.\n",
+ "\n",
+ "We don't have time to do a deep dive on all these libraries in this book, so we'll just be touching on some of the main parts of each. For a far more in depth discussion, we strongly suggest Wes McKinney's [Python for Data Analysis, 2nd ed](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/ref=asap_bc?ie=UTF8). Wes is the creator of Pandas, so you can be sure that the information is accurate!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will be looking at the Blue Book for Bulldozers Kaggle Competition: \"The goal of the contest is to predict the sale price of a particular piece of heavy equipment at auction based on its usage, equipment type, and configuration. The data is sourced from auction result postings and includes information on usage and equipment configurations.\"\n",
+ "\n",
+ "This is a very common type of dataset and prediction problem, and similar to what you may see in your project or workplace."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Kaggle Competitions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Kaggle is an awesome resource for aspiring data scientists or anyone looking to improve their machine learning skills. There is nothing like getting hands-on practice and receiving real-time feedback to help you improve your skills.\n",
+ "\n",
+ "Kaggle provides:\n",
+ "\n",
+ "1. Interesting datasets\n",
+ "2. Feedback on how you're doing\n",
+ "3. A leader board to see what's good, what's possible, and what's state-of-art.\n",
+ "4. Blog posts by winning contestants sharing useful tips and techniques.\n",
+ "\n",
+ "Until now all our datasets have been available to download through fastai's integrated dataset system. However, the dataset we will be using in this chapter is only available from Kaggle. Therefore, you will need to sign up to Kaggle, then you need to go to the [page for the competition](https://www.kaggle.com/c/bluebook-for-bulldozers). On that page click on \"rules\", and then \"I understand and accept\". (Although the competition has finished, and you will not be entering it, you still have to agree to the rules to be allowed to download the data).\n",
+ "\n",
+ "The easiest way to download Kaggle datasets is to use the Kaggle API. You can install this using pip by running this in a notebook cell:\n",
+ "\n",
+ " !pip install kaggle\n",
+ "\n",
+ "You need an API key to use the Kaggle API; to get one, go to \"my account\" on the Kaggle website, and click \"create new API token\". This will save a file called `kaggle.json` to your PC. We need to create this on your GPU server. To do so, open the file you downloaded, copy the contents, and paste them inside `''` below, e.g.: `creds = '{\"username\":\"xxx\",\"key\":\"xxx\"}'`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "creds = ''"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...then execute this cell (only needs to be run once):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred_path = Path('~/.kaggle/kaggle.json').expanduser()\n",
+ "if not cred_path.exists():\n",
+ " cred_path.parent.mkdir(exist_ok=True)\n",
+ " cred_path.write(creds)\n",
+ " cred_path.chmod(0o600)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now you can download datasets from Kaggle! We'll pick a path to download the dataset to:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Path('/home/jhoward/.fastai/archive/bluebook')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path = URLs.path('bluebook')\n",
+ "path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and use the Kaggle API to download the dataset to that path, and extract it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#7) [Path('TrainAndValid.csv'),Path('Machine_Appendix.csv'),Path('random_forest_benchmark_test.csv'),Path('Test.csv'),Path('median_benchmark.csv'),Path('ValidSolution.csv'),Path('Valid.csv')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "if not path.exists():\n",
+ " path.mkdir()\n",
+ " api.competition_download_cli('bluebook-for-bulldozers', path=path)\n",
+ " file_extract(path/'bluebook-for-bulldozers.zip')\n",
+ "\n",
+ "path.ls(file_type='text')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Look at the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Kaggle provides info about some of the fields of our dataset; on the [Kaggle Data info](https://www.kaggle.com/c/bluebook-for-bulldozers/data) page they say that the key fields in `train.csv` are:\n",
+ "\n",
+ "- SalesID: the unique identifier of the sale\n",
+ "- MachineID: the unique identifier of a machine. A machine can be sold multiple times\n",
+ "- saleprice: what the machine sold for at auction (only provided in train.csv)\n",
+ "- saledate: the date of the sale\n",
+ "\n",
+ "In any sort of data science work, it's **important to look at your data directly** to make sure you understand the format, how it's stored, what type of values it holds, etc.. Even if you've read descriptions about your data, the actual data may not be what you expect. We'll start by reading the training set into a Pandas DataFrame; note that we have to tell Pandas which columns contain dates. Generally it's a good idea to also specify `low_memory=False` unless Pandas actually runs out of memory and returns an error. The `low_memory` parameter, which is `True` by default, tells Pandas to only look at a few rows of data at a time to figure out what type of data is in each column. This means that Pandas can actually end up using a different data type for different rows, which generally leads to data processing errors or model training problems later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(path/'TrainAndValid.csv', low_memory=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['SalesID', 'SalePrice', 'MachineID', 'ModelID', 'datasource',\n",
+ " 'auctioneerID', 'YearMade', 'MachineHoursCurrentMeter', 'UsageBand',\n",
+ " 'saledate', 'fiModelDesc', 'fiBaseModel', 'fiSecondaryDesc',\n",
+ " 'fiModelSeries', 'fiModelDescriptor', 'ProductSize',\n",
+ " 'fiProductClassDesc', 'state', 'ProductGroup', 'ProductGroupDesc',\n",
+ " 'Drive_System', 'Enclosure', 'Forks', 'Pad_Type', 'Ride_Control',\n",
+ " 'Stick', 'Transmission', 'Turbocharged', 'Blade_Extension',\n",
+ " 'Blade_Width', 'Enclosure_Type', 'Engine_Horsepower', 'Hydraulics',\n",
+ " 'Pushblock', 'Ripper', 'Scarifier', 'Tip_Control', 'Tire_Size',\n",
+ " 'Coupler', 'Coupler_System', 'Grouser_Tracks', 'Hydraulics_Flow',\n",
+ " 'Track_Type', 'Undercarriage_Pad_Width', 'Stick_Length', 'Thumb',\n",
+ " 'Pattern_Changer', 'Grouser_Type', 'Backhoe_Mounting', 'Blade_Type',\n",
+ " 'Travel_Controls', 'Differential_Type', 'Steering_Controls'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's a lot of columns for us to look at! Try looking through the dataset to get a sense of what kind of information is in each one. We'll shortly see how to \"zero in\" on the most interesting bits.\n",
+ "\n",
+ "At the point, a good next step is to handle *ordinal columns*. This refers to columns containing strings or similar, but where those strings have a natural ordering. For instance, here are the levels of `ProductSize`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([nan, 'Medium', 'Small', 'Large / Medium', 'Mini', 'Large', 'Compact'], dtype=object)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['ProductSize'].unique()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can tell Pandas about a suitable ordering of these levels like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sizes = 'Large','Large / Medium','Medium','Small','Mini','Compact'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['ProductSize'] = df['ProductSize'].astype('category')\n",
+ "df['ProductSize'].cat.set_categories(sizes, ordered=True, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's important to note what metric is being used for a project. Generally, selecting the metric is an important part of the project setup. In many cases, choosing a good metric will require more than just selecting a variable that already exists. It is more like a design process. You should think carefully about which metric, or set of metric, actually measures the notion of model quality which matters to you. If no variable represents that metric, you should say if you can build the metric from the variables which are available.\n",
+ "\n",
+ "However, in this case Kaggle tells us what metric to use: RMSLE (root mean squared log error) between the actual and predicted auction prices. Here we need do only a small amount of processing to use this: we take the log of the prices, so that m_rmse of that value will give us what we ultimately need."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dep_var = 'SalePrice'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[dep_var] = np.log(df[dep_var])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Decision trees"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Decision tree ensembles, as the name suggests, rely on decision trees. So let's start there! A decision tree asks a series of binary (that is, yes or no) questions about the data, and on that basis makes a prediction. For instance, the first question might be \"was the equipment manufactured before 1990?\" The second question will depend on the result of the first question (which is why this is a tree); for equipment manufactured for 1990 the second question might be \"was the auction after 2005?\" And so forth…\n",
+ "\n",
+ "This sequence of questions is now a procedure for taking any data item, whether an item from the training set or a new one, and assigning that item to a group. Namely, after asking and answering the questions, we can say the item belongs to the group of all the other training data items which yielded the same set of answers to the questions. But what good is this? the goal of our model is to predict values for items, not to assign them into groups from the training dataset. The value of this is that we can now assign a prediction value for each of these groups--for regression, we take the target mean of the items in the group.\n",
+ "\n",
+ "Let's consider how we find the right questions to ask. Of course, we wouldn't want to have to create all these questions ourselves — that's what computers are for! The basic steps to train a decision tree can be written down very easily:\n",
+ "\n",
+ "1. Loop through each column of the dataset in turn\n",
+ "1. For each column, loop through each possible level of that column in turn\n",
+ "1. Try splitting the data into two groups, based on whether they are greater than or less than that value (or if it is a categorical variable, based on whether they are equal to or not equal to that level of that categorical variable)\n",
+ "1. Find the average sale price for each of those two groups, and see how close that is to the actual sale price of each of the items of equipment in that group. That is, treat this as a very simple \"model\" where our predictions are simply the average sale price of the item's group\n",
+ "1. After looping through all of the columns and possible levels for each, pick the split point which gave the best predictions using our very simple model\n",
+ "1. We now have two different groups for our data, based on this selected split. Treat each of these as separate datasets, and find the best split for each, by going back to step one for each group\n",
+ "1. Continue this process recursively, and until you have reached some stopping criterion for each group — for instance, stop splitting a group further when it has only 20 items in it.\n",
+ "\n",
+ "Although this is an easy enough algorithm to implement yourself (and it is a good exercise to do so) we can save some time by using the implementation built into sklearn.\n",
+ "\n",
+ "But even before using sklearn, we have to prepare our data somewhat before we can use it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> A: Here's a productive question to ponder. If you consider that the procedure for defining decision tree essentially chooses _sequence of splitting questions about variables_, you might ask yourself, how do we know this procedure chooses the _correct sequence_? The rule is to choose the splitting question which produces the best split, and then to apply the same rule to groups that split produces, and so on (this is known in computer science as a \"greedy\" approach). Can you imagine a scenario in which asking a “less powerful” splitting question would enable a better split down the road (or should I say down the trunk!) and lead to a better result overall?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Handling dates"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first piece of data preparation we need to do is to enrich our representation of dates. The fundamental basis of the decision tree which we just described is bisection -- dividing up a group into two. We look at the ordinal variables and divide up the dataset based on whether the variable's value is greater (or lower) than a threshhold, and we look at the categorical variables and divided up the datset based on whether the variable's level is a particular level. So this algorithm has a way of dividing up the dataset based on both ordinal and categorical data.\n",
+ "\n",
+ "How does this apply to a common data type, the date? You might want to treat a date as an ordinal value, because it is meaningful to say that one date is greater than another. However, dates are a bit different from most ordinal values in that some dates are qualitatively different from others in a way that that is often relevant to the systems we are modelling.\n",
+ "\n",
+ "So in order to help the above algorithm handle dates intelligently, we'd like our model to know more than whether a date is more recent or less recent. We might want our model to make decisions based on that date's day of week, on whether a day is a holiday, on what month it is in, and so forth. To do this, we replace every date column with a set of date metadata columns, such as holiday, day of week, and month. These columns provide categorical data that we suspect will be useful.\n",
+ "\n",
+ "Fastai comes with a function that will do this for us — we just have to pass a column name which contains dates:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = add_datepart(df, 'saledate')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's do the same for the test set while we're there:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = pd.read_csv(path/'Test.csv', low_memory=False)\n",
+ "df_test = add_datepart(df_test, 'saledate')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that there are now lots of new columns in our DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'saleYear saleMonth saleWeek saleDay saleDayofweek saleDayofyear saleIs_month_end saleIs_month_start saleIs_quarter_end saleIs_quarter_start saleIs_year_end saleIs_year_start saleElapsed'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "' '.join(o for o in df.columns if o.startswith('sale'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Using TabularPandas and TabularProc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A second piece of preparatory processing is to be sure we can handle strings and missing data. Out of the box, sklearn cannot do either. Instead we will use fastai's class `TabularPandas`, which wraps a Pandas data frame and provides a few conveniences. To populate a `TabularPandas`, we will use two `TabularProc`s, `Categorify` and `FillMissing`. A `TabularProc` is like a regular `Transform`, except that:\n",
+ "\n",
+ "- It returns the exact same object that's passed to it, after modifying the object *in-place*, and\n",
+ "- It runs the transform once, when data is first passed in, rather than lazily as the data is access.\n",
+ "\n",
+ "`Categorify` is a `TabularProc` which replaces a column with a numeric categorical column. `FillMissing` is an `TabularProc` which replaces missing values with the median of the column, and creates a new boolean column that is set to True for any row where the value was missing. These two transforms are needed for nearly every tabular dataset you will use, so it's a good starting point for your data processing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "procs = [Categorify, FillMissing]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`TabularPandas` will also handle splitting into training vs validation datasets for us. \n",
+ "\n",
+ "We need to be very careful about our validation set here. In particular we want to design it so that it is like the *test set* which Kaggle will use to judge the contest.\n",
+ "\n",
+ "Recall the distinction between a validation set and a test set, as discussed in <| \n", + " | UsageBand | \n", + "fiModelDesc | \n", + "fiBaseModel | \n", + "fiSecondaryDesc | \n", + "fiModelSeries | \n", + "fiModelDescriptor | \n", + "ProductSize | \n", + "fiProductClassDesc | \n", + "state | \n", + "ProductGroup | \n", + "ProductGroupDesc | \n", + "Drive_System | \n", + "Enclosure | \n", + "Forks | \n", + "Pad_Type | \n", + "Ride_Control | \n", + "Stick | \n", + "Transmission | \n", + "Turbocharged | \n", + "Blade_Extension | \n", + "Blade_Width | \n", + "Enclosure_Type | \n", + "Engine_Horsepower | \n", + "Hydraulics | \n", + "Pushblock | \n", + "Ripper | \n", + "Scarifier | \n", + "Tip_Control | \n", + "Tire_Size | \n", + "Coupler | \n", + "Coupler_System | \n", + "Grouser_Tracks | \n", + "Hydraulics_Flow | \n", + "Track_Type | \n", + "Undercarriage_Pad_Width | \n", + "Stick_Length | \n", + "Thumb | \n", + "Pattern_Changer | \n", + "Grouser_Type | \n", + "Backhoe_Mounting | \n", + "Blade_Type | \n", + "Travel_Controls | \n", + "Differential_Type | \n", + "Steering_Controls | \n", + "saleIs_month_end | \n", + "saleIs_month_start | \n", + "saleIs_quarter_end | \n", + "saleIs_quarter_start | \n", + "saleIs_year_end | \n", + "saleIs_year_start | \n", + "SalesID_na | \n", + "MachineID_na | \n", + "ModelID_na | \n", + "datasource_na | \n", + "auctioneerID_na | \n", + "YearMade_na | \n", + "MachineHoursCurrentMeter_na | \n", + "saleYear_na | \n", + "saleMonth_na | \n", + "saleWeek_na | \n", + "saleDay_na | \n", + "saleDayofweek_na | \n", + "saleDayofyear_na | \n", + "saleElapsed_na | \n", + "SalesID | \n", + "MachineID | \n", + "ModelID | \n", + "datasource | \n", + "auctioneerID | \n", + "YearMade | \n", + "MachineHoursCurrentMeter | \n", + "saleYear | \n", + "saleMonth | \n", + "saleWeek | \n", + "saleDay | \n", + "saleDayofweek | \n", + "saleDayofyear | \n", + "saleElapsed | \n", + "SalePrice | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Low | \n", + "521D | \n", + "521 | \n", + "D | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "Wheel Loader - 110.0 to 120.0 Horsepower | \n", + "Alabama | \n", + "WL | \n", + "Wheel Loader | \n", + "#na# | \n", + "EROPS w AC | \n", + "None or Unspecified | \n", + "#na# | \n", + "None or Unspecified | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "2 Valve | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "None or Unspecified | \n", + "None or Unspecified | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "Standard | \n", + "Conventional | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "1139246 | \n", + "999089 | \n", + "3157 | \n", + "121 | \n", + "3.0 | \n", + "2004 | \n", + "68.0 | \n", + "2006 | \n", + "11 | \n", + "46 | \n", + "16 | \n", + "3 | \n", + "320 | \n", + "1163635200 | \n", + "11.097410 | \n", + "
| 1 | \n", + "Low | \n", + "950FII | \n", + "950 | \n", + "F | \n", + "II | \n", + "#na# | \n", + "Medium | \n", + "Wheel Loader - 150.0 to 175.0 Horsepower | \n", + "North Carolina | \n", + "WL | \n", + "Wheel Loader | \n", + "#na# | \n", + "EROPS w AC | \n", + "None or Unspecified | \n", + "#na# | \n", + "None or Unspecified | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "2 Valve | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "23.5 | \n", + "None or Unspecified | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "Standard | \n", + "Conventional | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "1139248 | \n", + "117657 | \n", + "77 | \n", + "121 | \n", + "3.0 | \n", + "1996 | \n", + "4640.0 | \n", + "2004 | \n", + "3 | \n", + "13 | \n", + "26 | \n", + "4 | \n", + "86 | \n", + "1080259200 | \n", + "10.950807 | \n", + "
| 2 | \n", + "High | \n", + "226 | \n", + "226 | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "Skid Steer Loader - 1351.0 to 1601.0 Lb Operating Capacity | \n", + "New York | \n", + "SSL | \n", + "Skid Steer Loaders | \n", + "#na# | \n", + "OROPS | \n", + "None or Unspecified | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "Auxiliary | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "None or Unspecified | \n", + "None or Unspecified | \n", + "None or Unspecified | \n", + "Standard | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "#na# | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "1139249 | \n", + "434808 | \n", + "7009 | \n", + "121 | \n", + "3.0 | \n", + "2001 | \n", + "2838.0 | \n", + "2004 | \n", + "2 | \n", + "9 | \n", + "26 | \n", + "3 | \n", + "57 | \n", + "1077753600 | \n", + "9.210340 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " SalesID SalePrice MachineID ModelID ... saleDay_na saleDayofweek_na \\\n",
+ "0 1139246 11.097410 999089 3157 ... 1 1 \n",
+ "1 1139248 10.950807 117657 77 ... 1 1 \n",
+ "2 1139249 9.210340 434808 7009 ... 1 1 \n",
+ "\n",
+ " saleDayofyear_na saleElapsed_na \n",
+ "0 1 1 \n",
+ "1 1 1 \n",
+ "2 1 1 \n",
+ "\n",
+ "[3 rows x 79 columns]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "to.items.head(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The conversion of categorical columns to numbers is done by simply replacing each unique level with a number. The numbers associated with the levels are chosen consecutively as they are seen in a column. So there's no particular meaning to the numbers in categorical columns after conversion. The exception is if you first convert a column to a pandas ordered category (as we did for `ProductSize` above), in which case the ordering you chose is used. We can see the mapping by looking at the `classes` attribute:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#7) ['#na#','Large','Large / Medium','Medium','Small','Mini','Compact']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "to.classes['ProductSize']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since it takes a minute or so to process the data to get to this point, we should save it - that way in the future we can continue our work from here without rerunning the previous steps. fastai provides a `save` method that uses Python's *pickle* system to save nearly any Python object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(path/'to.pkl').save(to)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To read this back later, you would type:\n",
+ "\n",
+ "```python\n",
+ "to = (path/'to.pkl').load()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating the decision tree"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can read in our data (only needed if you're coming back to this notebook after a break, and don't want to recreate the `TabularPandas` object), and define our independent and dependent variables."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "to = (path/'to.pkl').load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xs,y = to.train.xs,to.train.y\n",
+ "valid_xs,valid_y = to.valid.xs,to.valid.y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that our data is all numeric, and there are no missing values, we can create a decision tree:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m = DecisionTreeRegressor(max_leaf_nodes=4)\n",
+ "m.fit(xs, y);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see what it's learned, we can display the tree. To keep it simple, we've told sklearn to just create four *leaf nodes*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ "| \n", + " | SalesID | \n", + "SalePrice | \n", + "MachineID | \n", + "ModelID | \n", + "... | \n", + "saleDay_na | \n", + "saleDayofweek_na | \n", + "saleDayofyear_na | \n", + "saleElapsed_na | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1139246 | \n", + "11.097410 | \n", + "999089 | \n", + "3157 | \n", + "... | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 1 | \n", + "1139248 | \n", + "10.950807 | \n", + "117657 | \n", + "77 | \n", + "... | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
| 2 | \n", + "1139249 | \n", + "9.210340 | \n", + "434808 | \n", + "7009 | \n", + "... | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "
3 rows × 79 columns
\n", + ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see the impact of `n_estimators`, let's get the predictions from each individual tree in our forest (these are in the `estimators_` attribute):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds = np.stack([t.predict(valid_xs) for t in m.estimators_])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, `preds.mean(0)` gives the same results as our random forest:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.233502"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "r_mse(preds.mean(0), valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> question: Why does this give the same result as our random forest?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's how RMSE improves as we add more and more trees. As you can see, the improvement levels off quite a bit after around 30 trees:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot([r_mse(preds[:i+1].mean(0), valid_y) for i in range(40)]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Out-of-bag error"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Is our validation set worse than our training set because we're over-fitting, or because the validation set is for a different time period, or a bit of both? With the existing information we've shown, we can't tell. However, random forests have a very clever trick called *out-of-bag (OOB) error* which can handle this (and more!)\n",
+ "\n",
+ "The idea is to calculate error on the training set, but only include the trees in the calculation of a row's error where that row was *not* included in training that tree. This allows us to see whether the model is over-fitting, without needing a separate validation set.\n",
+ "\n",
+ "This also has the benefit of allowing us to see whether our model generalizes, even if we have such a small amount of data that we want to avoid removing items to create a validation set. The OOB predictions are available in the `oob_prediction_` attribute. Note that we compare to *training* labels, since this is being calculated on the OOB trees on the training set.\n",
+ "\n",
+ "> A: My intuition for this is that, since every tree was trained with a different randomly selected subset of rows, out-of-bag error is a little like imagining that every tree therefore also has its own validation set. That validation set is simply the rows that were notselected for that tree's training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.210686"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "r_mse(m.oob_prediction_, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that our OOB error is much lower than our validation set error. This means that something else is causing our error, in *addition* to normal generalization error. We'll discuss the reasons for this later in this chapter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> question: Make a list of reasons why this model's validation set error on this dataset might be worse than the OOB error. How could you test your hypotheses?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model interpretation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For tabular data, model interpretation is particularly important. For a given model, the things we are most likely to be interested in are:\n",
+ "\n",
+ "- How confident are we in our projections using a particular row of data?\n",
+ "- For predicting with a particular row of data, what were the most important factors, and how did they influence that prediction?\n",
+ "- Which columns are the strongest predictors, which can we ignore?\n",
+ "- Which columns are effectively redundant with each other, for purposes of prediction?\n",
+ "- How do predictions vary, as we vary these columns?\n",
+ "\n",
+ "As we will see, random forests are particularly well suited to answering these questions. Let's start with the first one!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Tree variance for prediction confidence"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We saw how the model averages predictions across the trees to get a prediction, that is, an estimate of the value. But how can we know the confidence of the estimate? One simple way is to use the standard deviation of predictions across the trees, instead of just the mean. This tells us the *relative* confidence of predictions. That is, for rows where trees give very different results, you would want to be more cautious of using those results, compared to cases where they are more consistent.\n",
+ "\n",
+ "In the last lesson we saw how to get predictions over the validation set, using a Python list comprehension to do this for each tree in the forest:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds = np.stack([t.predict(valid_xs) for t in m.estimators_])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(40, 7988)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "No we have a prediction for every tree and every auction, for 160 trees and 7988 auctions in the validation set.\n",
+ "\n",
+ "Using this we can get the standard deviation of the predictions over all the trees, for each auction:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preds_std = preds.std(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here are the prediction standard deviations for the first 5 auctions, that is, the first 5 rows of the validation set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.21529149, 0.10351274, 0.08901878, 0.28374773, 0.11977206])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds_std[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, the confidence of the predictions varies widely. For some auctions, there is a low standard deviation because the trees agree. For others, it's higher, as the trees don't agree. This is information that would be useful to use in a production setting; for instance, if you were using this model to decide what items to bid on at auction, a low-confidence prediction may cause you to look more carefully into an item before you made a bid"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Feature importance"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's not normally enough to just to know that a model can make accurate predictions -- we also want to know *how* it's making predictions. The most important way to see this is with *feature importance*. We can get these directly from sklearn's random forest, by looking in the `feature_importances_` attribute. Here's a simple function we can use to pop them into a DataFrame and sort them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def rf_feat_importance(m, df):\n",
+ " return pd.DataFrame({'cols':df.columns, 'imp':m.feature_importances_}\n",
+ " ).sort_values('imp', ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The feature importances for our model show that the first few most important columns have a much higher importance score than the rest, with (not surprisingly) `YearMade` and `ProductSize` being at the top of the list:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " cols imp\n",
+ "69 YearMade 0.182890\n",
+ "6 ProductSize 0.127268\n",
+ "30 Coupler_System 0.117698\n",
+ "7 fiProductClassDesc 0.069939\n",
+ "66 ModelID 0.057263\n",
+ "77 saleElapsed 0.050113\n",
+ "32 Hydraulics_Flow 0.047091\n",
+ "3 fiSecondaryDesc 0.041225\n",
+ "31 Grouser_Tracks 0.031988\n",
+ "1 fiModelDesc 0.031838"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fi = rf_feat_importance(m, xs)\n",
+ "fi[:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A plot of the feature importances shows the relative importances more clearly:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | cols | \n", + "imp | \n", + "
|---|---|---|
| 69 | \n", + "YearMade | \n", + "0.182890 | \n", + "
| 6 | \n", + "ProductSize | \n", + "0.127268 | \n", + "
| 30 | \n", + "Coupler_System | \n", + "0.117698 | \n", + "
| 7 | \n", + "fiProductClassDesc | \n", + "0.069939 | \n", + "
| 66 | \n", + "ModelID | \n", + "0.057263 | \n", + "
| 77 | \n", + "saleElapsed | \n", + "0.050113 | \n", + "
| 32 | \n", + "Hydraulics_Flow | \n", + "0.047091 | \n", + "
| 3 | \n", + "fiSecondaryDesc | \n", + "0.041225 | \n", + "
| 31 | \n", + "Grouser_Tracks | \n", + "0.031988 | \n", + "
| 1 | \n", + "fiModelDesc | \n", + "0.031838 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def plot_fi(fi):\n",
+ " return fi.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False)\n",
+ "\n",
+ "plot_fi(fi[:30]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The way these importances are calculated is quite simple yet elegant. The feature importance algorithm loops through each tree, and then recursively explores each branch. At each branch, it looks to see what feature was used for that split, and how much the model improves as a result of that split. The improvement (weighted by the number of rows in that group) is added to the importance score for that feature. This is added across all branches of all trees, and finally the scores are normalized such that they add to 1.0."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Removing low-importance variables"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It seems likely that we could use just a subset of the columns and still get good results. Let's try just keeping those with a feature importance greater than 0.005."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "21"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "to_keep = fi[fi.imp>0.005].cols\n",
+ "len(to_keep)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll retrain our model, using just this subset of the columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xs_imp = xs[to_keep]\n",
+ "valid_xs_imp = valid_xs[to_keep]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m = rf(xs_imp, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and here's the result:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.181208, 0.232323)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m_rmse(m, xs_imp, y), m_rmse(m, valid_xs_imp, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our accuracy is about the same, but we have far less columns to study:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(78, 21)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(xs.columns), len(xs_imp.columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've found that generally the first step to improving a model is simplifying it. 78 columns are too many for us to study them all in depth! Furthermore, in practice often a simpler, more interpretable model is easier to roll out and maintain.\n",
+ "\n",
+ "It also makes our feature importance plot easier to interpret. Let's look at it again:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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4LiIiIiJi5UmaaftvXhMZtkvRIiIiIiIimpXEJiIiIiIi2t5wf8dmjWV7YqtjiIiIiIhoF5mxiYiIiIiItpfEJiIiIiIi2l4Sm4iIiIiIaHtJbCIiIiIiou1l84AYEHfNXsjE06a1OowhMSsfIo2IiIgYdjJjExERERERbS+JTYtI+rSk7w/xNd8i6f6hvGZERERExFBIYjNIJC2q/ZZLWlo7P9L2F20fOwjX3UnSLyXNl7RA0kxJ+wHY/rXt7Qf6mhERERERrZZ3bAaJ7VHdx5JmAcfavr6ZvpJeZvvFlbz0tcD3gP3L+a6AVnKsiIiIiIi2kBmbFpF0uqSp5XiiJEv6gKRHgBtK+W6SbikzL7+XNLmPMccDWwPn236+/H5j++ZSP1nSo+X40IZZpeckzSh160j6mqRHJD0u6VxJ6/VwveMldUrqXLZk4QA+nYiIiIiI/kliM7zsCewI7C1pAjANOBMYB3wCuErSRivo/xTwADBV0sGSNu6toe0rbI8qM0ubAX8CLivVXwG2A14HTAImAJ/rYYzzbHfY7hgxckw/bzUiIiIiYuAksRleTre92PZS4CjgOtvX2V5uezrQCezXW2fbBt4KzAL+A5gr6SZJ2/bWR9JawKXADNv/KUnAccDHbM+z/QzwReCwAbrHiIiIiIgBl3dshpeu2vFWwCGSDqiVrQ38vxUNYPtR4GQASVsA5wEXA2/qpctZwGjgw+V8I2AkMLPKcYDqHZ0RTd9FRERERMQQS2IzvLh23AVMsX3cSg9md0n6Dn9dYvYSkg4DDgd2tf1CKX4SWArsZHt2s9faZcIYOvPhyoiIiIhokSxFG76mAgdI2lvSCEnrlpf/N++tg6QNJZ0haZKktcpmAv8M3NpD278Dvg0cbPuJ7nLby4Hzga9LemVpO0HS3gN8fxERERERAyaJzTBluws4CPg08ATVDM4nWfHf2fPAROB64Gngf4HngPf10PYgYEPg5trOaD8vdadSbUJwq6Sny3j5/k1EREREDFuq3jePWDUdHR3u7OxsdRgRERERsZqTNNN2R2N5ZmwiIiIiIqLtZfOANiRpUS9V+9r+9ZAGExERERExDCSxaUPlo5oREREREVFkKVpERERERLS9JDYREREREdH2shQtBsRdsxcy8bRprQ5jyMzKx0gjIiIihpXM2Awzki6UdOYK6hdJetVQxhQRERERMdwlsVkJkmZJel7S+IbyOyVZ0sTBurbtUbb/tKrj1BMoSRNL3N0f6nxc0s8kvWPVI46IiIiIGHxJbFbeQ8Dh3SeSdgHWa104A2Js2XHttcB04MeS3tfakCIiIiIi+pbEZuVNAY6pnb8XuLj7RNK7JP2PpKcldUk6vd5Z0h6SbpG0oNS/r1a9oaRpkp6R9DtJ29T6WdKkcnyhpO+soO0OkqZLmifpfknvaebGbD9m+5vA6cBXJOXfSUREREQMa/kP68q7FdhA0o6SRgCHAlNr9YupEp+xwLuAEyUdDCBpS+DnwLeBjYDXAXfW+h4OnAFsCDwAnLWCOHpsK2l9qlmXS4FXlnbflbRTP+7x6tJ3+54qJR0vqVNS57IlC/sxbERERETEwEpis2q6Z23eAdwHzO6usD3D9l22l9v+A3AZsGepPhK43vZltl+w/ZTtemJzte3bbL8IXEKV+PSmt7b7A7NsX2D7Rdt3AFcB7+7H/c0pf47rqdL2ebY7bHeMGDmmH8NGRERERAysbPe8aqYANwFbU1uGBiDpjcCXgZ2BlwPrAD8s1VsAD65g3Mdqx0uAUSvRdivgjZIW1OpfVmJu1oTy57x+9ImIiIiIGHKZsVkFth+m2kRgP6plW3WXAj8FtrA9BjgXUKnrArZhcHUBN9oeW/uNsn1iP8b4B+DPwP2DE2JERERExMDIjM2q+wCwoe3FkurPczQwz/azkt4AHAH8stRdAny6vMx/NTCGKgGqL0dbVT8DvizpaODyUvY6YJHte1fUUdLGwCHA54GP2F7e18V2mTCGzny0MiIiIiJaJDM2q8j2g7Y7e6g6Cfh3Sc8AnwOurPV5hGqW5xSqZV53Um2xPJBxPQO8EziM6l2Zx4CvUC2J680CSYuBu0p8h9j+r4GMKyIiIiJiMMh2q2OI1UBHR4c7O3vK7yIiIiIiBo6kmbY7GsszYxMREREREW0viU1ERERERLS9JDYREREREdH2kthERERERETbS2ITERERERFtL4lNRERERES0vXygMwbEXbMXMvG0aa0OY1iZlQ+WRkRERAyZYTNjI2l7Sf8j6RlJyyX92yBf73RJU5tsO0PSsYMZz8qQdLekya2OIyIiIiKi1YZNYgN8Cphhe7TttWx/AUDSZEmWdHW9saTXlvIZQxmkpPdJWiZpUfk9JOkCSdsNZRwAtneyPaOZtpJmSdprkEOKiIiIiGiJ4ZTYbAXc3UvdE8Dukl5RK3sv8MdBj6pnv7U9ChgD7AUsBWZK2nkoLi5pSJcQDvX1IiIiIiL6a1gkNpJuAN4KnFNmQS6VdGatyfPANcBhpf0I4D3AJQ3j7C7pdkkLy5+71+q2lnRjWeo2HRjf0Hc3SbdIWiDp980s8bK9zPaDtk8CbgROb2a8MuvzpxLLQ5KOrNUdJ+neUnePpNeX8lmSTpX0B2CxpJfVZ2HK0rofSbqi9L1D0mtL3RRgS+Da8nw/VcoPLMvZFpTldjvW4vib6/X1PCIiIiIiWmVYJDa23wb8Gji5zIQ830Ozi4FjyvHeVLM7c7orJY0DpgHfAl4BnA1Mq83yXArMpEpovkA149Pdd0LpeyYwDvgEcJWkjfpxG1cDb+lrPEnrlxj3tT0a2B24s/Q7hCo5OgbYADgQeKp2jcOBdwFjbb/YQwwHAT8s17wUuEbS2raPBh4BDrA9yvZXy9K5y4CPAhsB11ElPi9v9nqSjpfUKalz2ZKF/XhUEREREREDa1gkNs2wfQswTtL2VP/xv7ihybuA/7M9xfaLti8D7gMOkLQlsCvwb7afs30TcG2t71HAdbavs73c9nSgE9ivHyHOoUoomhlvObCzpPVsz7XdvQTvWOCrtm935QHbD9eu8S3bXbaX9hLDTNs/sv0CVWK3LrBbL20PBabZnl7afw1YjyrRaup6ts+z3WG7Y8TIMb09l4iIiIiIQdc2iU0xBTiZatnajxvqNgMebih7GJhQ6ubbXtxQ120r4JCyJGuBpAXAHsCm/YhtAjCvr/FKDIcCJwBzJU2TtEPptwXw4Aqu0dVHDH+pt70ceJTq3nvykudV2neV+2j2ehERERERw0K7vTcxBXgAuNj2Ekn1ujlUCUXdlsB/A3OBDSWtX0tutgRcjruAKbaPW4XY/oFqOV2f49n+BfALSetRLVc7n2oZWxewzQqu4RXUQZUYASBpLWBz/rpcr7HvHGCXWnuV/rP7cb2IiIiIiGGhrRIb2w9J2hP4Uw/V1wHflnQEcCXwT8CrgZ/ZflJSJ3CGpE8DbwAOAH5a+k4Fbpe0N3A9sDbVEq4HbD/aWzxlE4MtgY8Dk4E39TUe8ALwRuBXVLupLQKWlX7fB86WdDNwB1WS80LDcrQV+XtJ/1ju68PAc8Ctpe5x4FW1tlcCp0l6O3AT8JHS/pYmr/USu0wYQ2c+SBkRERERLdJuS9GwfbPtOT2UPwXsD5xC9cL9p4D9bT9ZmhxBlVDMAz5P7R0d211UL95/mmpr6S7gk/T+fN4kaRHwNDCD6kX/XW3f1cR4a5UY55RY9gROKv1+CJxF9eL/M1Q7wXW/t9OMn1Atc5sPHA38Y3l/BuBLwGfL0rhP2L6f6l2gbwNPUiV6B9juaeOGiIiIiIhhTXZWG60OJJ0OTLJ9VCuu39HR4c7OzlZcOiIiIiLWIJJm2u5oLG+7GZuIiIiIiIhGSWwiIiIiIqLttdXmAdE726e3OoaIiIiIiFbJjE1ERERERLS9JDYREREREdH2kthERERERETbS2ITERERERFtL5sHtNhgf39GkoFtbT8g6Vxgtu0vDPR17pq9kImnTRvoYVdrs778rlaHEBEREbHayIzNSpA0S9JeDWXvk3Rzq2Jqhu0TBiOpiYiIiIhotSQ2w5ikzKhFRERERDQhic0Ak/RJSVc1lH1b0jfK8daSbpT0jKTpwPhau4mSLOkDkh4BbijlP5T0mKSFkm6StFOtzwxJx9bOe505knShpDNr5wdJulPS05IelLRPbYw/lRgfknTkwDydiIiIiIjBkcRm4E0F9pE0Fv4y63IoMKXUXwrMpEpovgC8t4cx9gR2BPYu5z8HtgVeCdwBXLKqQUp6A3Ax8ElgLPD/AbMkrQ98C9jX9mhgd+DOXsY4XlKnpM5lSxauakgRERERESstS51W3jWSXqydvxy4w/ZcSTcBhwDnA/sAT9qeKWlLYFdgL9vPATdJuraHsU+3vbj7xPZ/dR+XzQbmSxpje1WyiQ8A/2V7ejmfXcZfH1gO7CzpEdtzgbk9DWD7POA8gHU23darEEtERERExCrJjM3KO9j22O4fcFKt7iKge5ezo/jrbM1mwPx60gI83MPYXd0HkkZI+nJZKvY0MKtUje+hX39sATzYWFhiOxQ4AZgraZqkHVbxWhERERERgyqJzeC4BniNpJ2B/fnr0rG5wIZlVqTblj30r89+HAEcBOwFjAEmlnKVPxcDI2vtN2kyxi5gm54qbP/C9juATYH7qGaeIiIiIiKGrSQ2g8D2s8CPqN6nuc32I6X8YaATOEPSyyXtARzQx3CjgeeAp6gSmC821N8J/KOkkZImUS0xa8YPgPdLeruktSRNkLSDpI0lHViSr+eARcCyJseMiIiIiGiJvGMzeC4CjgX+uaH8iFI3D/gt1Qv8Y1cwzsVUmwjMLn3+DTixVv91qvd2Hgf+QDU7tBd9sH2bpPeX/luX/h8EFgKnUC2fM1XidFJv43TbZcIYOvPByYiIiIhoEdl553swlI0C7gM2sf10q+MZbB0dHe7s7Gx1GBERERGxmpM003ZHY3mWog0CSWsBHwcuXxOSmoiIiIiIVstStAFW3k15nGq3s31aHE5ERERExBohic0AK9slj2p1HBERERERa5IsRYuIiIiIiLaXxCYiIiIiItpeEpuIiIiIiGh7SWwiIiIiIqLtZfOAYUqSgW1tP9DqWJpx1+yFTDxtWqvDWK3MygdPIyIiIpqWGZs2JmljSU9KmtxQfoGky1oUVkRERETEkMuMTRuz/bikjwHnS3qN7aWS3g68C9hpIK8l6WW2XxzIMSMiIiIiBkpmbIaApFMlzZb0jKT7Jb1d0hsk/VbSAklzJZ0j6eW99F9H0tckPSLpcUnnSloPwPYU4H7g30vZfwIftv1E6bu5pB9LekLSQ5I+WBv3TZJurcXwLUlrl7qXSbKkkyQ9ANw3yI8pIiIiImKlJbEZZJK2B04GdrU9GtgbmAUsAz4GjAfeBLwdOKmXYb4CbAe8DpgETAA+V6s/Afhn4HLgf21fXq49AvgZcHvp8w7gk2VWB+BF4CMlhjcD+wD/0nDtA4FdgV16uLfjJXVK6ly2ZGETTyMiIiIiYnAksRl8y4B1gFdLWtv2LNsP2p5p+1bbL9qeRTXTsmdjZ0kCjgM+Znue7WeALwKHdbex/ShVorMXcGKt+27ABra/aPv5shHBD7r72r7d9u9KDH8Czushhi/anm97aWNsts+z3WG7Y8TIMSv5eCIiIiIiVl3esRlkth+Q9FHgdGAnSb8APg6MAs4GOoCRVH8XM3sYYqNSP7PKcQAQMKKh3d3AfNtza2VbAVtKWlArGwHMAJC0A/AfwN/XYvhdw7hdTd5qRERERETLZMZmCNi+1PYeVImGqZaWfY/qvZVtbW8AfJoqYWn0JLAU2Mn22PIbY3tUE5fuAv6v1m+s7dG2Dyj1/wn8LzCpxPC5HmJwP283IiIiImLIJbEZZJK2l/Q2SesAz1IlKcuA0cDTwKIyc3JiT/1tLwfOB74u6ZVlzAmS9m7i8r8Fnpd0iqR1JY2QtIukvy/1o4GFwGJJO/K379dERERERLSFLEUbfOsAXwZ2BF4AbgGOp9oE4DzgU8D/AFcAb+tljFOpZlNulTQemE014/OLFV3Y9ouS9qNa8vbJEsu9wGdKk1OAc6lmi+4oMVETrAQAACAASURBVOyxMje5y4QxdOaDkhERERHRIrKz0ihWXUdHhzs7O1sdRkRERESs5iTNtN3RWJ6laBERERER0faS2ERERERERNtLYhMREREREW0viU1ERERERLS9JDYREREREdH2kthERERERETbS2ITERERERFtLx/orJG0PXA51ccz1wc+b/sLrY1q4EkysK3tB/poNxmYanvzvsa8a/ZCJp42bYAijEaz8vHTiIiIiBXKjM1LfQqYYXu07bW6kxpJkyUtl7So/GZLOmOwg5E0Q5Ilvbah/JpSPnmwY4iIiIiIaAdJbF5qK+DuXurm2B5lexSwB/ABSQcPQUx/BI7pPpH0CmA34IkhuHZERERERFtIYlNIugF4K3BOmZW5VNKZPbW1/RBwC/DqWv9vSuqS9LSkmZLeUqt7g6TOUve4pLNrdbtJukXSAkm/72EW5hLgUEkjyvnhwI+B52tjrCPpG5LmlN83JK1Tq/+kpLml7p8b7nsdSV+T9EiJ7VxJ6/Xv6UVEREREtFYSm8L224BfAyeXWZnne2sraVvgzcCtteLbgdcB44BLgR9KWrfUfRP4pu0NgG2AK8s4E4BpwJml3yeAqyRtVBt3DnAP8M5yfgxwcUNIn6GaxXkd8FrgDcBnyzX2KeO+A9gW2Kuh71eA7UrfScAE4HO93XvDczi+JGydy5YsbKZLRERERMSgSGLTvM3KrMrTVMvDfgfc3F1pe6rtp2y/aPs/gHWA7Uv1C8AkSeNtL7LdnRAdBVxn+zrby21PBzqB/RqufTFwTNncYKzt3zbUHwn8u+0/234COAM4utS9B7jA9v/aXgyc3t1JkoDjgI/Znmf7GeCLwGHNPBDb59nusN0xYuSYZrpERERERAyKJDbNm2N7bJl1GQssBS7qrpR0iqR7JS2UtAAYA4wv1R+gmhW5T9LtkvYv5VsBh5SEaUHptwewacO1rwbeBnwImNJDbJsBD9fOHy5l3XVdDXXdNgJGAjNr1//vUh4RERER0Tay3fNKsL1Q0qXAFQDlfZpTgbcDd9teLmk+oNL+/4DDJa0F/CPwo7IJQBcwxfZxfVxviaSfAydSLWVrNIeXbnywZSkDmAtsUWu7Ze34SaoEbSfbs5u6+YiIiIiIYSgzNitB0iiq5VrdicRo4EWqncpeJulzwAa19kdJ2sj2cmBBKV4GTAUOkLS3pBGS1i1bS/f03ZhPA3vantVD3WXAZyVtJGk81TsyU0vdlcD7JL1a0kjg892dSjznA1+X9MoS6wRJe/f7oUREREREtFBmbJq3maRF5fg5qo0DjiznvwB+TvXuzWLg67x0+dc+wNklsXgYOMz2s0CXpIOAr1IlJ8uA26hmZl7C9hz+OgvT6EyqROoP5fyHpQzbP5f0DeAGYDnVpgJH1vqeSpUI3VqSotnA98o9NW2XCWPozEckIyIiIqJFZLvVMcRqoKOjw52dna0OIyIiIiJWc5Jm2u5oLM9StIiIiIiIaHtJbCIiIiIiou0lsYmIiIiIiLaXxCYiIiIiItpeEpuIiIiIiGh7SWwiIiIiIqLtJbGJiIiIiIi2lw90tilJs4BjbV/f6lgA7pq9kImnTWt1GLECs/IB1YiIiFiNZcamxSTtIekWSQslzZP0G0m7DtG1Z0g6thxPlrRc0qLye1TSlUMVS0RERETEqkhi00KSNgB+BnwbGAdMAM4AnmtRSHNsjwJGA7sB9wG/lvT2FsUTEREREdGUJDattR2A7ctsL7O91PYvbf9B0jaSbpD0lKQnJV0iaWxPg0haS9Jpkh4s7a+UNK7UrStpailfIOl2SRuvKChXHrX9OeD7wFcG+sYjIiIiIgZSEpvW+iOwTNJFkvaVtGGtTsCXgM2AHYEtgNN7GefDwMHAnqX9fOA7pe69wJjS/xXACcDSfsR4NfB6Ses3Vkg6XlKnpM5lSxb2Y8iIiIiIiIGVxKaFbD8N7AEYOB94QtJPJW1s+wHb020/Z/sJ4GyqxKUn/wJ8psyyPEeVAL1b0suAF6gSmkllVmhmuW6z5lAlWX8zW2T7PNsdtjtGjBzTjyEjIiIiIgZWEpsWs32v7ffZ3hzYmWrG5RuSXinpckmzJT0NTAXG9zLMVsCPy1KzBcC9wDJgY2AK8AvgcklzJH1V0tr9CHECVeK1YOXuMCIiIiJi8CWxGUZs3wdcSJXgfIkqoXiN7Q2Ao6hmTnrSBexre2ztt67t2bZfsH2G7VcDuwP7A8f0I6x/AO6wvXglbysiIiIiYtAlsWkhSTtIOkXS5uV8C+Bw4FaqnckWAQskTQA+uYKhzgXOkrRVGWcjSQeV47dK2kXSCOBpqqVpy/qIS5ImSPo8cCzw6VW60YiIiIiIQZYPdLbWM8AbgY+XHc8WUG3//Emql/0vBhYCD1AtKftYL+N8k2o255eSNgP+DFwB/ATYhCrx2ZwqUbqCallbTzaTtKiMtRC4BZhs+9a+bmSXCWPozAcgIyIiIqJFZLvVMcRqoKOjw52dna0OIyIiIiJWc5Jm2u5oLM9StIiIiIiIaHtJbCIiIiIiou0lsYmIiIiIiLbXdGIj6XBJO5bj7SXdJOkGSTsMXngRERERERF968+MzZnAvHL8NeA24CbguwMdVERERERERH/0Z7vnjWw/LmldYA/g3VTfRHlyUCKLiIiIiIhoUn8SmyckTQJ2AW63/ZykkVTfPImIiIiIiGiZ/iQ2XwBmUn21/tBS9nbg9wMdVKtI2h64HJgErA983vYXBvF6pwOTbB/VRNsZwFTb3x+seFbFXbMXMvG0aa0OI4aRWflga0RERAyhpt+xsX0hsCmwue3ppfh3wGGDEFerfAqYYXu07bW6kxpJkyVZ0tX1xpJeW8pnDGWQkt4naZmkReX3kKQLJG03lHFERERERAwXK0xsJK1V/wHPAs/Wzp8E/jwUgQ6RrYC7e6l7Athd0itqZe8F/jjoUfXst7ZHAWOAvYClwExJO7conoiIiIiIlulrxuZFqg0Cevt117c9STcAbwXOKbMgl0o6s9bkeeAaygyVpBHAe4BLGsbZXdLtkhaWP3ev1W0t6UZJz0iaDoxv6LubpFskLZD0e0mT+4rb9jLbD9o+CbgROL2Z8cqsz59KLA9JOrJWd5yke0vdPZJe3+cDjIiIiIhoob4Sm62BV63g113f9my/Dfg1cHKZCXm+h2YXA8eU472pZnfmdFdKGgdMA74FvAI4G5hWm+W5lOo9pfFU7yy9t9Z3Qul7JjAO+ARwlaSN+nEbVwNv6Ws8SeuXGPe1PRrYHbiz9DuEKjk6BtgAOBB4qh8xREREREQMuRVuHmD74caysgRtY+Bx28sHK7DhyPYtksaVTQaOoUp01qs1eRfwf7anlPPLJH0YOKDMCO0K7GX7OeAmSdfW+h4FXGf7unI+XVInsB9wUZMhzqFKYvoa70fAcmBnSY/YngvMLe2OBb5q+/Zy/kBvF5N0PHA8wIgN+pN/RUREREQMrKY3D5C0gaSLqd6zmQ0slXSRpDGDFt3wNAU4mWrZ2o8b6jYDGpPBh4EJpW6+7cUNdd22Ag4py8YWSFpA9b2gTfsR2wT++hHVXscrMRwKnADMlTRN0g6l3xbAg81czPZ5tjtsd4wYuab9M4iIiIiI4aTpxIZq6dL6wM5UsxS7ACNL+ZpkCnAS1WzIkoa6OVQJRd2WVIngXGDDsgysXtetC5hie2ztt77tL/cjtn+gWk7X53i2f2H7HVSJ033A+bV+2/TjmhERERERLdefxGYf4Gjbf7T9nO0/Au8v5WsM2w8BewKf6aH6OmA7SUdIepmkQ4FXAz8ry/o6gTMkvVzSHsABtb5TqZas7S1phKR1yzbTm68ontJ2a0nfBiYDZ/Q1nqSNJR1YkqzngEVU3ycC+D7wCUl/r8okSY3JWkRERETEsNKfD3Q+C2zES5dPjaf6j/EaxfbNvZQ/JWl/4JvA96jeT9nf9pOlyRFU78vMA35L9Y7O2NK3S9JBwFeBy6gSjduAE3sJ402SFgGi2nZ7BrCr7XubGG8t4BSq2SdTbRxwUun3w7LZwaVUS9tmAUfzt0vsXmKXCWPozAcZIyIiIqJFZLu5htJnqV6YP5vqP7lbAR8DpnZ/yDLWXB0dHe7s7Gx1GBERERGxmpM003ZHY3l/ZmzOonpX5EiqF+HnUO2e9YOBCTEiIiIiImLl9Ocdm28C99vey/arbe8F3CvpG4MUW0RERERERFP6k9gcTvXye91MqvdGIiIiIiIiWqY/iY2BEQ1lI/o5RkRERERExIDrT1Lya+ALktYCKH+ezl+/mxIREREREdES/dk84CPAz6i+VP8w1ccl5/LSb7FEREREREQMuaYTG9uPSno98AZgC6ov1N9me/lgBRcREREREdGMpr9jE7Ei62y6rTd9bzbIi8E1Kx+BjYiIWOP19h2bvPg/TEmaLOnRVscREREREdEOktgMEEmzJC2VtKj2O6fVcUVERERErAn6s3lA9O0A29e3OoiVJWmE7WWtjiMiIiIior8yYzPIJL1P0s2SviZpvqSHJO1bqx8n6QJJc0r9Nb2Ms6OkGZIWSLpb0oG1uv0k3SPpGUmzJX2ifu2GcSxpUjm+UNL3JF0naTHwVknrlFgfkfS4pHMlrTcoDyciIiIiYoAksRkabwTuB8YDXwV+IEmlbgowEtgJeCXw9cbOktYGrgV+Wdp8CLhE0valyQ+Af7E9GtgZuKEfsR0BnAWMBm4GvgJsB7wOmARMAD7XU0dJx0vqlNS5bMnCflwyIiIiImJgJbEZWNeUGZXu33Gl/GHb55dlXhcBmwIbS9oU2Bc4wfZ82y/YvrGHcXcDRgFftv287Ruovil0eKl/AXi1pA3KOHf0I+af2P5N2bb7OeA44GO259l+BvgicFhPHW2fZ7vDdseIkWP6ccmIiIiIiIGVxGZgHWx7bO13fil/rLuB7SXlcBTV94Dm2Z7fx7ibAV0N3wx6mGo2BeCfgP2AhyXdKOlN/Yi5q3a8EdXs0czu5Az471IeERERETFsJbFprS5gnKSxfbSbA2whqf73tSUwG8D27bYPolqmdg1wZWmzmCpRAUDSJj2MXf+Q0ZPAUmCnWnI2xvao/txURERERMRQy65oLWR7rqSfA9+V9EFgEfAm2zc1NP0dVZLyKUn/AbwZOADYVdLLgUOAn9leKOlpoHtns98DO0l6HXAfcHof8SyXdD7wdUkn2/6zpAnAzrZ/saK+u0wYQ2c+nhgRERERLZIZm4F1bcN3bH7cRJ+jqd6RuQ/4M/DRxga2nwcOpHof50ngu8Axtu+rjTGrJDUnAEeVfn8E/h24Hvg/qs0B+nIq8ABwaxnvemD7FXeJiIiIiGgt2e67VUQfOjo63NnZ2eowIiIiImI1J2mm7Y7G8szYRERERERE20tiExERERERbS+JTUREREREtL0kNhERERER0faS2ERERERERNtLYhMREREREW0vH+iMAXHX7IVMPG1aq8OI6LdZ+bBsRETEamHYz9hI2l7S/0h6RtJySf/W6piaJWmGpGNbHUdERERExOpu2Cc2wKeAGbZH217L9he6KyR9WtJDkhZJelTSFS2Mc0iVpOnZkvA9LWmmpNMkrdPq2CIiIiIihlo7JDZbAXc3Fkp6L3A0sJftUUAH8Kshjm1QSGp2ieDJtkcDmwKnAIcB10nSoAUXERERETEMDevERtINwFuBc8qszKWSzizVuwK/sP0ggO3HbJ9X6ztG0g8kzZU0W9KZkkbU6o+TdG+Z8bhH0utL+Y5lNmSBpLslHVjrc6Gk70iaVvr9TtI2tfp3SLpP0kJJ5wCq1W0j6QZJT0l6UtIlksbW6mdJOlXSH4DFkj4p6aqG5/FtSd9ofE62F9ueARwIvAl4V2m/VpnFebBc90pJ40rdupKmlvIFkm6XtHGpGyfpAklzJM2XdE3//uYiIiIiIobWsE5sbL8N+DXVzMQo4Pla9a3AMSUB6KgnLcVFwIvAJODvgHcCxwJIOgQ4HTgG2IAqIXhK0trAtcAvgVcCHwIukbR9bdzDgTOADYEHgLPKmOOBq4DPAuOBB4E31/oJ+BKwGbAjsEWJoe5wqqRkLDAV2Kc7+SmzOIcCU1bwvB4BOoG3lKIPAwcDe5brzge+U+reC4wpcbwCOAFYWuqmACOBncpz+HpP15N0vKROSZ3LlizsLayIiIiIiEE3rBObFbE9lSrx2Bu4EfizpNMAyszDvsBHy2zGn6n+c35Y6X4s8FXbt7vygO2Hgd2AUcCXbT9v+wbgZ1QJR7erbd9m+0XgEuB1pXw/4B7bP7L9AvAN4LFavA/Ynm77OdtPAGdTJRx137LdZXup7bnATcAhpW4f4EnbM/t4NHOAceX4X4DP2H7U9nNUidS7S5L0AlVCM8n2MtszbT8tadPy7E6wPd/2C7Zv7OlCts+z3WG7Y8TIMX2EFRERERExeNp6u2fbl1DNqKxNNTNxiaT/oZqZWBuYW3vdZC2gqxxvQTWj0mgzoMv28lrZw8CE2vljteMlVInQX/rWYrOkv5xLeiXwLarZlNElnvkN1+9qOL8IOBE4HziKFczW1EwAbinHWwE/llS/n2XAxmWsLYDLy6zQVOAzpWye7cbYIiIiIiKGrbadsakrswo/BP4A7EyVIDwHjLc9tvw2sL1T6dIFbNPDUHOALSTVn8uWwOwmwphLlRQA/P/t3XmcXFWd/vHPQ2QLCYkQtkRCRGBkc1AbFBVBxWFxw3EddgcE3BVUFvXHooAyCi7oMKCC7KAoqFEBUTZZpIMoA4IDJCEkYQkhKxAgeX5/3NNwKas7naTS1ZU879frvrx1zj3nnnu401PfnKUoC/g3ruWfDBh4le21qQKVxkX+bvh8OfAqSdsA76QaIeqVpI2B11JN34PqOfeo9cFI22vYnlr67HjbWwFvKPXvX8qsU1//ExEREREx2HXsiI2kA4HHqKZrzaeakrY1cKvt6ZKuAr5VfvdmHvBy4GVlWtUPgVMl3QjcThXkPAvcWur6oqRvUa2ReRfVRgWLM55qk4N/B34JfALYsJY/HJgNzJI0BvjC4iq0/bSknwEXAn8ua2ia9cXQ0sbTgD8DvylZZwAnSjrA9mRJ6wFvsH2FpLcAM4C7gTnl+ReWvvst8ANJn6Dqux1tX99XW7cdM4Lu/NBhRERERLRJJ4/YzAGOAR4EZgGnAB+zfWPJ3x9YjeqL+xPAz6i2RaaM7pxIFTDMpRoZWcf2M1QbCexB9aX/B8D+tu9ZXGNsz6BaD/N14HFgc+BPtUuOB15DFdyMB37ez+f8CbAtzaehnS5pLvAI1Zqey4Dda1PpvkMVZF1VrrsFeF3J25CqT+YAf6dap3R+yduPKtC5B3gU+Gw/2xoRERER0RayG2c/xWAiaSxVgLGh7Tntbk9vurq63N3d3e5mRERERMQKTtIE212N6Z08YrPCK2t9DgcuHsxBTUREREREu3XsGpsVnaS1qKaYTaba6jkiIiIiInqRwGaQsj2fF7aSjoiIiIiIPmQqWkREREREdLwENhERERER0fES2ERERERERMfLGptoiTunzmbcUePb3YyItpuUH6qNiIhoi4zYtIgkS9qsBfVcK+ngVrSpFSTtIumhdrcjIiIiIqIvCWwGWAkUFkma13Ds2O62RURERER0qkxFa49ptl/W7kZERERERKwoMmLThKQjJU2VNFfSvZLeJmkHSTdLmiVpuqTTJa3WS/nVJX1T0oOSHpF0hqQ1l6Idr5D0B0mPS5oh6QJJI2v5kyQdLeluSU9IOlvSGiVvlKRfl/bOlHSDpFVK3mhJl0l6TNJESZ+u1bmmpHNKfXcD2y9xB0ZEREREDLAENg0k/QvwSWB728OB3YBJwELgc8AoYEfgbcDHe6nmG8AWwHbAZsAY4P8tTXOAk4HRwJbAxsBxDdfsU9r4inLPL5f0I4CHgPWADYBjAJfg5lfAX0u73gZ8VtJupdyxpa5XlHoP6LVx0iGSuiV1L3xy9lI8XkREREREaySw+WcLgdWBrSStanuS7fttT7B9i+3nbE8C/gfYubGwJAEfBT5ne6btucBJwIdrl40uIyn1Y63GumzfZ/tq2wtsPwac2uSep9ueYnsmcCLwHyX9WWAjYBPbz9q+wbapRmDWs32C7WdsPwCcVWvfB4ETS9unAN/traNsn2m7y3bXkKEj+ujSiIiIiIjlK2tsGti+T9JnqUZGtpZ0JXA4MIwqsOgChlL13YQmVaxX8idUMQ5QjbwMqV3TrzU2ktanCix2AoZTBaJPNFw2pXY+mWp0B+C/yjNcVdpxpu2vA5tQAqtauSHADeV8dJM6IyIiIiIGtYzYNGH7QttvogoCTDW17L+Be4DNba9NNbVLTYrPAJ4CtrY9shwjbA9biqacXO7/qnLPfZvcc+Pa+VhgWnmGubaPsL0p8C7gcElvowpaJtbaNtL2cNt7ljqmN6kzIiIiImJQy4hNg7LGZgzwJ+BpqiBlFaoRkznAPEmvBD4GPNZY3vYiSWcBp0n6pO1HJY0BtrF95RI2ZzgwG5hV6vhCk2s+IenXwJNUwdYl5TneSRWI3V/avbAcfwbmSDqSajToGar1O2vavg24FDha0q3AWsCn+tPQbceMoDs/TBgRERERbZIRm3+2OvB1qpGXh4H1qQKGzwN7A3Op1qRc0kcdRwL3AbdImgP8HviXWv7oJr9j874m9RwPvIYquBkP/LzJNRcCVwEPlONrJX3zct95wM3AD2xfa3sh1QjOdsDE8pw/BHoWyRxPNf1sYqn3vD6eMyIiIiJiUFC1njw6kaRJwMG2f9/utnR1dbm7u7vdzYiIiIiIFZykCba7GtMzYhMRERERER0vgU1ERERERHS8bB7QwWyPa3cbIiIiIiIGg4zYREREREREx0tgExERERERHS+BTUREREREdLyssYmWuHPqbMYdNb7dzYgY1CblR2wjIiKWm4zYdDhJ4yRZ0mKDVEkHSrpxINoVERERETGQEtgMMEmTJD0jaVRD+h0lQBk3gG2xpM3K+XGSnpU0txz/kHS6pI0Gqj0REREREUsrgU17TAT+o+eDpG2BNdvXnOddYns4sA7wXmBDYEKCm4iIiIgY7BLYtMd5wP61zwcA5/Z8kDRC0rmSHpM0WdKXJa1S8oZI+qakGZIeAF40ab+U/ZGk6ZKmSvqapCFL0jjbz9q+C/gQ8BhwxFI+Z0RERETEgEhg0x63AGtL2rIEHR8Czq/lfw8YAWwK7EwVBH2k5H0UeCfwaqALeH9D3T8BngM2K9f8G3Dw0jTS9kLgCmCnZvmSDpHULal74ZOzl+YWEREREREtkcCmfXpGbd4O3ANMLek9gc7RtufangR8C9iv5H8Q+LbtKbZnAif3VChpA2AP4LO259t+FDgN+PAytHMa1dS0f2L7TNtdtruGDB2xDLeIiIiIiFg22e65fc4DrgdeTm0aGjAKWA2YXEubDIwp56OBKQ15PTYBVgWmS+pJW6Xh+iU1Bpi5DOUjIiIiIpa7BDZtYnuypInAnsBBtawZwLNUQcrdJW0sL4zoTAc2rl0/tnY+BVgAjLL93LK2sazreRfw+2WtKyIiIiJiecpUtPY6CHir7fm1tIXApcCJkoZL2gQ4nBfW4FwKfFrSyyS9FDiqp6Dt6cBVwLckrS1pFUmvkLTzkjRK0qqStgQuotoZ7dSlfcCIiIiIiIGQEZs2sn1/L1mfotpA4AHgaeAs4Mcl7yxgC+CvwBzgm8Bba2X3B75ONdozvNTxjX426UOS9gJEtbbmauC1tqctruC2Y0bQnV9Vj4iIiIg2ke12tyFWAF1dXe7u7m53MyIiIiJiBSdpgu2uxvRMRYuIiIiIiI6XwCYiIiIiIjpeApuIiIiIiOh4CWwiIiIiIqLjJbCJiIiIiIiOl8AmIiIiIiI6XgKbiIiIiIjoeCv1D3RKOg7YzPa+y6l+A5vbvk/SGcBU219dHvfq5f7nAA/Z/vLyvtedU2cz7qjxy/s2ESutSfkB3IiIiD513IiNpEmSdm1IO1DSje1qU3/YPmx5BDXl2RdKmlc7Tm/1fSIiIiIiBrOVesSmL5JeYvu5drejn262/aZ2NyIiIiIiol06bsSmL5K+IOmyhrTvSfp2OX+5pOskzZV0NTCqdt04SZZ0kKQHgT+U9J9KeljSbEnXS9q6VuZaSQfXPvc6ciTpHElfq31+j6Q7JM2RdL+k3Wt1PFDaOFHSPq3pHZD0UUn3SZop6ZeSRpf04yV9r5yvKmm+pFPK5zUlPS3ppa1qR0REREREq61QgQ1wPrC7pJFQjboAHwLOK/kXAhOoApqvAgc0qWNnYEtgt/L5t8DmwPrA7cAFy9pISTsA5wJfAEYCbwYmSVoL+C6wh+3hwBuAO5b1fuWebwVOBj4IbARMBi4u2dcBu5Tz7YGHqfoBYEfgXttPNKnzEEndkroXPjm7Fc2MiIiIiFgqnToV7XJJ9WliqwG3254u6XrgA8BZwO7ADNsTJI2l+tK+q+0FwPWSftWk7uNsz+/5YPvHPedls4EnJI2wvSzf5A8Cfmz76vJ5aql/LWARsI2kB21PB6b3o77XS5pV+7y77Vsartmn3PP2cq+jy7OMA24GNpe0LlWQ9SPg45KGUQU41zW7qe0zgTMBVt9oc/ejnRERERERy0WnjtjsZXtkzwF8vJb3E6Bnl7N9eWG0ZjTwRD1ooRq1aDSl50TSEElfL1PF5gCTStaoJuWWxMbA/Y2JpW0fAg4DpksaL+mV/ajvlnp/NAlqoHr+55/X9jzgcWCM7aeAbqog5s1UgcxNwBvpI7CJiIiIiBgsOjWw6cvlwKskbQO8kxemjk0HXlpGRXqMbVK+PvKwN/AeYFdgBDCupKv873xgaO36DfvZxinAK5pl2L7S9tuppovdQzXy1ArTgE16PpR+WJcyWkQVvLwVeDVwW/m8G7ADcH2L2hARERERsVyshP7fNAAAIABJREFUcIGN7aeBn1Gtp/mz7QdL+mSqUYnjJa0m6U3AuxZT3XBgAdXIxlDgpIb8O4B/lzRU0mZUU8z640fARyS9TdIqksZIeqWkDSS9uwQdC4B5wMJ+1rk4F5Z7bidp9fIst9qeVPKvA/YH7rb9DHAtcDAw0fZjLWpDRERERMRy0alrbBbnJ1Rfyv+zIX3vkjeTal3JuVSL93tzLtWoxdRS5ivAx2r5p1Gt23kE+BvV6NCuLIbtP0v6SCn/8lL+E8Bs4Aiq6XOmCpw+3ls9S8L2NZK+AlwGvJRqqtmHa5fcBKzJC6MzdwNP08/Rmm3HjKA7PyAYEREREW0ie8Vb8102CrgH2ND2nHa3Z2XQ1dXl7u7udjcjIiIiIlZwkibY7mpMX+GmoklaBTgcuDhBTURERETEymGFCmzK2pQ5wNuBY9vcnJaRdIakeU2OM9rdtoiIiIiIwWCFWmNTtkse1u52tJrtw6i2gI6IiIiIiCZWqBGbiIiIiIhYOSWwiYiIiIiIjpfAJiIiIiIiOl4Cm4iIiIiI6Hgr1OYBy4skA5vbvq/dbRms7pw6m3FHjW93MyKinyblB3UjImIF03EjNpL2ltRdtjueLum3kt7U7nYtDUnHSJpYnuUhSZcsY327SHqoVe2LiIiIiOgUHRXYSDoc+DZwErABMBb4AfCedrarN5J6HRGTdACwH7Cr7WFAF3DNQLUtIiIiImJF0jGBjaQRwAnAJ2z/3PZ828/a/pXtL0haXdK3JU0rx7clrV7KHijpxob6LGmzcn5O+RHMqyXNlXSdpE16acfqkr4p6UFJj5Rya5a8XcrIy5GSHgbO7uORtgeutH0/gO2HbZ9Z6vmApAkN9z1C0uXlfE9Jd5e2TpX0+fLjpL8FRtd+wHO0pFUkHSXpfkmPS7pU0jqlnnGlHz4iaYqkJyQdJml7SX+TNEvS6Uv4nyoiIiIiYsB1TGAD7AisAfyil/wvAa8HtgP+FdgB+PIS1L8P8FVgFHAHcEEv130D2KLcZzNgDPD/avkbAusAmwCH9HG/W4D9JX1BUpekIbW8XwIvl7RlLW1f4Lxy/iPgUNvDgW2AP5QfJ90DmGZ7WDmmAZ8G9gJ2BkYDTwDfb2jL64DNgQ9RjYh9CdgV2Br4oKSdmz2ApEPKtMDuhU/O7uNRIyIiIiKWr04KbNYFZth+rpf8fYATbD9q+zHgeKqpXv013vb1thdQfbHfUdLG9QskCfgo8DnbM23PpZoW9+HaZYuAY20vsP1UbzezfT7wKWA34DrgUUlHlbwFwCVUwQyStgbGAb8uxZ8FtpK0tu0nbN/ex3MdCnzJ9kOl3uOA9zdMk/uq7adtXwXMBy4q/TgVuAF4dS/PcKbtLttdQ4aO6KMJERERERHLVycFNo8Do/pYtzIamFz7PLmk9deUnhPb84CZTcqvBwwFJpRpWrOA35X0Ho/Zfro/N7R9ge1dgZHAYcAJknYr2T8B9i7B1H7ApSUwAXgfsCcwuUyb27GP22wC/KLW3r8DC6nWKPV4pHb+VJPPw/rzPBERERER7dJJgc3NwNNU06qamUb1Jb7H2JIG1SjE0J4MSRs2Kb9xLX8Y1XSyaQ3XzKD6or+17ZHlGFEW//dwP57lRcpaoZ8Cf6OaWobtW4BngJ2AvXlhGhq2b7P9HmB94HLg0j7uPQXYo9bekbbXKKMxERERERErhI4JbGzPplrL8n1Je0kaKmlVSXtIOgW4CPiypPUkjSrXnl+K/xXYWtJ2ktagmo7VaE9Jb5K0GtVam1ttT6lfYHsRcBZwmqT1ASSNqY2y9FvZ0OAdkoaXBf57UK1pubV22bnA6cBztm8s5VaTtI+kEbafBeZQjcBANdKybtlooccZwIk9myGU/hmUu8hFRERERCytjvqBTtunSnqEalOAC4C5wATgROB2YG2qUQ+AnwJfK+X+IekE4PdUIy5HU609qbsQOJZqk4LbqdbsNHMkVdB0SwmgpgL/DVy5hI8zBziGKvgaQjV17mM9AUxxHlWQ9dWGsvsBp5cNB+6lrMWxfY+ki4AHSt5WwHcAAVdJGg08SrV+54olbG+fth0zgu784F9EREREtInsJZ45tcKRdA7wkO0l2UVtuSvbSD8KvMb2/7W7PX3p6upyd3d3u5sRERERESs4SRNsdzWmd8xUtJXUx4DbBntQExERERHRbh01Fa3TSDqGarpZoxts77GYspOoppD1tllCREREREQUCWwA2wcup3pPovqdm6UpO661rYmIiIiIWHFlKlpERERERHS8BDYREREREdHxEthERERERETHS2ATEREREREdL5sHREvcOXU2444a3+5mRMQgNik/4hsREctRRmxWUpLOl3Rcu9sREREREdEKHR/YSPqwpFslzZf0aDn/uCS1u21LQtJYSfNqh8sz9Xzeqd1tjIiIiIgYrDo6sJF0BPAd4L+ADYENgMOANwKrNbl+yIA2sAlJTaf/2X7Q9rCeoyT/ay3thiZ1tf15IiIiIiIGg44NbCSNAE4APm77Z7bnuvIX2/vYXiDpHEn/Lek3kuYDb5E0QtK5kh6TNFnSlyWtUuo8TtL5tXuMKyMnLymfD5T0gKS5kiZK2qd27X9K+rukJyRdKWmTWp4lfULS/wH/twzPfL6k70v6XXmenSS9W9IdpU0PSvpKQ5k3S7pF0mxJUyTt16TetSVdL+k0Vd5ZnmWupIckfa6X9hwiqVtS98InZy/tY0VERERELLNO3jxgR2B14IrFXLc3sCfwTqpRnDOBEcCmwLrAVcB04Ed9VSJpLeC7wPa275W0EbBOydsLOAZ4F1XgchRwEfCGWhV7Aa8Dnur3E/b9PLcCq1KNTu0L3A1sC1wj6S+2fy3p5cB44CDg58BI4GUNzzUK+B3wa9vHlbSzgffYvknSOsC4Zg2xfSZVf7L6Rpt7GZ8rIiIiImKpdeyIDTAKmGH7uZ4ESTdJmiXpKUlvLslX2P6T7UXAs8CHgKPLCM8k4FvAP41i9GIRsI2kNW1Pt31XST8UONn230t7TgK2q4/alPyZtpc1sPmF7ZttL7K9wPYfbP9v+fxX4GJg53LtvsDvbF9q+znbM2zfUatrDHAdcEFPUFM8C2wlaXhp8+3L2OaIiIiIiOWqkwObx4FR9TUrtt9ge2TJ63m2KbUyo6hGbSbX0iZTfcHvk+35VEHRYcB0SeMlvbJkbwJ8pwRVs4CZgBrqnUJrvKgeSTtKurZMrZsNHEz1nAAbA/f3Ude7qUZ9zmpIf2/Je7DU/brWND0iIiIiYvno5MDmZmAB8J7FXFefIjWDajSiPpIyFphazucDQ2t5G76oIvtK228HNgLu4YWAYApwqO2RtWNN2zf10o5l0VjPxcBlwMa2RwA/pAqqetr1ij7qOgP4IzBe0vPPbftW2+8G1gd+Xe4RERERETFodewaG9uzJB0P/KBs7fw74EngVcBavZRZKOlS4ERJ+1OtkTkc+Ga55A7gSEljgdnA0T1lJW1AtUbmGqp1MvOAhSX7DOCrku6wfVfZ2ODfbP+0pQ/d3HBgpu2nJb0e+DBVMAJwPvA3Se+jWos0EhhTpqxBFSQdBvwY+KWkd1IFRe+lWnMzR9Lc2nP2atsxI+jOj+9FRERERJt08ogNtk+hCky+CDwKPAL8D3AkcFMvxT5FNTLzAHAjcCHVF3tsXw1cAvwNmMALAQJUfXUEMI1qqtnOwMdLuV8A3wAuljQH+F9gjxY95uJ8DDi5BCDHAJf2ZNieSLWhwZGlzbdTbTBA7RpTbS7wKPALqql6BwCTy7McRP/XIEVEREREtIWq77URy6arq8vd3d3tbkZERERErOAkTbDd1Zje0SM2ERERERER0MFrbDqVpJ2A3zbLsz1sgJsTEREREbFCSGAzwGzfACSAiYiIiIhooUxFi4iIiIiIjpfAJiIiIiIiOl4Cm4iIiIiI6HgDssZG0r9Q/Xr9ZlQ/nnms7a8OwH3PAR6y/eXlfa8m9zawue37Bvre7XDn1NmMO2p8u5sRER1uUn7oNyIiltJAjdh8EbjW9nDbq/QENZJ2kbRI0jxJcyXdK+kjA9SmfpE0TpIlvaQhfSNJP5I0vbT9HknHS1prANp0oKSFpd/mSZoo6WxJWyzve0dEREREDEYDFdhsAtzVS960ss3x2sCRwFmStmq8qDGwaCdJ6wA3A2sCO9oeDrwdGAm8YoCacXPptxHArsBTwARJ2wzQ/SMiIiIiBo3lHthI+gPwFuD0MrpwoaSvNV7nyuXAE8BWtZGSgyQ9CPyh1PduSXdJmiXpWklb1u71akm3lxGUS4A1ankHSrqxoW2WtFk5X1PStyRNljRb0o2S1gSuL5fPKu3fETgcmAvsa3tSaf8U25+x/bcmffAOSX+RNEfSFEnH1fLWkHS+pMfLM90maYNamx8ozzNR0j5N+m2h7fttfxy4DqjX/XpJN5V6/yppl4b+aFq3pI9K+nvJu1vSaxrvGxERERExmCz3wMb2W4EbgE+WEYZnml0naRVJ76Ua9bizlrUzsCWwW5lqdRHwWWA94DfAryStJmk14HLgPGAd4KfA+5agqd8EXgu8oZT/IrAIeHPJH2l7mO2bqUZIfm57UT/rng/sX57tHcDHJO1V8g6gGnXZGFgXOAx4qkxp+y6wRxkRegNwx2Lu83NgJwBJY4DxwNfK83weuEzSen3VLekDVMHR/lSjaO8GHu/nc0ZEREREtMVg2BVttKRZwAzgWGA/2/fW8o+zPd/2U8CHgPG2r7b9LFUwsibVF/PXA6sC37b9rO2fAbf1pwGSVgH+E/iM7allFOQm2wt6KbIuML2/D2j7Wtt32l5URnQuogrYAJ4t9W1W7jvB9pyStwjYRtKatqfb7m06X49pVEEMwL7Ab2z/ptz3aqAb2HMxdR8MnGL7tjKKdp/tyc1uJukQSd2Suhc+Obu/3RERERER0XKDIbCZZnuk7XVsb2f74ob8KbXz0cDzX7LLiMkUYEzJm2rbteubfiFvYhTVtLX7+3n948BG/bwWSa+T9EdJj0maTTUqM6pknwdcCVwsaZqkUyStans+VSB3GDBd0nhJr1zMrcYAM8v5JsAHyjS0WSV4fBOw0WLq3ph+9oPtM2132e4aMnREv/oiIiIiImJ5GAyBzeLUA5VpVF/YAZAkqi/iU6lGUMaUtB5ja+fzgaG1shvW8mYAT9N84b+bpP0eeG8Z6emPC4FfAhvbHgGcAQigjC4db3srqpGnd1JNA8P2lbbfThVE3QOctZj7vJdq2h9UAd95JWjsOday/fXF1D2FgdsAISIiIiKiJTohsKm7FHiHpLdJWhU4AlgA3ES1S9lzwKclvUTSvwM71Mr+Fdha0naS1qC2yL6M/PwYOFXSaElDJO0oaXXgMappW5vW6jqVav3JTyRtAtWaFkmnSnpVk3YPB2baflrSDsDePRmS3iJpW0lDgDlUU9MWStqgbJSwVnnGecDCxopLW18u6XvALsDxJet84F2SdivXrKFqe+2XLabuHwKfl/RaVTbrecaIiIiIiMFq0Gyh3B+275W0L/A9qmlXdwDvsv0MQAlmzqJaMP8bqsX0PWX/IekEqtGWp4CjgUNr1X8eOJlqXc4wqkBoN9tPSjoR+FMJpna3fYukN5T73FoChKlUa2ea/SDnx4FvSTqdaueyS6k2EgDYkGoE52VUAcYlVEHJelSB23lUo0Z3lHp67ChpHtXIzwzgWmB7238vzztF0nuAU0q7FgJ/Bj5GFdA2rdv2TyWtSzXKNAaYBOzHYqb1bTtmBN35Yb2IiIiIaBO9eElKxNLp6upyd3d3u5sRERERESs4SRNsdzWmd9pUtIiIiIiIiH+SwCYiIiIiIjpeApuIiIiIiOh4CWwiIiIiIqLjJbCJiIiIiIiOl8AmIiIiIiI6XgKbiIiIiIjoeB31A50xeN05dTbjjhrf7mZEREQMqEn5ceqIQSMjNgNM0i6SHmrDfY+R9MOBvm9ERERExEBIYNMLSZMkPSVpnqRHJJ0taVi729VD0rWSDm5Ie4+kOyTNkTRD0jWSxgHYPsn2wc3qioiIiIjodAls+vYu28OA1wDbA1+uZ6oyKPpQ0mbAucARwAjg5cAPgEXtbFdERERExEAYFF/KBzvbU4HfAtuUkZITJf0JeBLYVNJoSb+UNFPSfZI+2lNW0pqSzpH0hKS7qQIkavkuQUnP53Mkfa32uT4Kc7+k3SWdCOwEnF5GlE4HtgMm2r7Glbm2L7P9YKnnOEnnl/Oecj3Hc5KOK3mjJV0m6TFJEyV9evn0akRERERE6ySw6QdJGwN7An8pSfsBhwDDgcnARcBDwGjg/cBJkt5Wrj0WeEU5dgMOWIL77kA1CvMFYCTwZmCS7S8BNwCftD3M9ieB24FXSjpN0lv6mjZnu6fcMOBNwBPAFWX06VfAX4ExwNuAz0rarZf2HSKpW1L3widn9/exIiIiIiJaLoFN3y6XNAu4EbgOOKmkn2P7LtvPARtSBQdH2n7a9h3AD6mCH4APAifanml7CvDdJbj/QcCPbV9te5HtqbbvaXah7QeAXagCkkuBGWX0p9cAR9J6wOXAp2z/hWo0aT3bJ9h+ptR5FvDhXu55pu0u211Dho5YgseKiIiIiGitbPfct71s/76eIAlgSi1pNDDT9txa2mSgq5Y/pSGvvzYGftPfi23fQhVIIWl74BLgS8DRjddKWhX4GXCh7YtL8ibA6BLM9RhCNToUERERETFoJbBZOq6dTwPWkTS8FtyMBaaW8+lUAcpdtby6J4Ghtc8bUk1rgyogekU/2vDPmfZtkn4ObNPLJd8D5vLiDRGmUK3T2byvuiMiIiIiBpsENsvI9hRJNwEnS/o8sAXVFLJ9yyWXAkdLuhVYC/hUQxV3AHtLugt4O7Az0F3yfgRcJenXwB+BjYDhZTraI8CmPZVIehOwJXCF7UclvRJ4N/CTxjZLOrTc53W267um/RmYI+lIqilzz5Q617R9W1/9sO2YEXTnR8oiIiIiok2yxqY1/gMYRzV68wvgWNtXl7zjqaafTQSuAs5rKPsZ4F3ALGAfqjUvANj+M/AR4DRgNtU6n01K9neA95fd1r5byr8buFPSPOB3pS2n9NLeTYFptZ3RjrG9sLRlu9LeGVTrhbKAJiIiIiIGNdl9zmiK6Jeuri53d3cv/sKIiIiIiGUgaYLtrsb0jNhERERERETHS2ATEREREREdL4FNRERERER0vAQ2ERERERHR8RLYREREREREx0tgExERERERHS8/0BktcefU2Yw7any7mxERERERy9mkQfqj7BmxWclJOl/Sce1uR0RERETEskhgswwkXSDpxw1pO0t6XNJGLb7X+ZIsac+G9NNL+r6tvF9ERERERCdJYLNsPg3sKentAJLWAM4CjrA9vVU3kTSknP4DOKCWvirwPuCBVt0rIiIiIqITJbBZBrYfBz4FnClpLeBY4H7b50haRdIxku6XNEPSxZJeClDyfibpYUmzJF0racueesvozPcl/U7SfGCnknU5sIukEeXzO4Bu4LFa2c0l/bGMGs2QdF7teiS9VtIdkuZKughYvf5Mkt4t6a+lXTdK2qblHRcRERER0WIJbJaR7Z8CE4CLgEOAQ0vW4VSBx5uBlwHzge/Wiv4a2BzYEPhf4LyGqvcGjgeGAzeXtKeA8cAHy+f9gXMbygn4GrARsBWwKfAVAEmrA1cAPwbWKed7PV9Q2p5qxOlgYN1y3RWSVmv27JIOkdQtqXvhk7ObXRIRERERMSAS2LTGJ4C3AifYfrCkHQocY3uq7aeB44APSlrF9iLb59ieW8t7bRn16fEL2zeXaxfU0s8F9pe0DvAG4Jf1htj+h+1rbD9j+1HgNGDnkv1GwMD3bD9r+2LgL7XihwA/sH2b7YW2e9YPbd/soW2fabvLdteQoSOaXRIRERERMSCy3XML2H5E0gzgrlryWOBXkhbVLwXWl/QYcDLwfmAU0HPNKKqRHYApvdzuOqoRoGOAK2wvkPR8pqQNqUaG3kg12rMKL0xVGw08ZNu1+ibXzjcB9pH0uVraasCYXtoSERERETEoZMRm+XkIeLvtkbVjDdsPU00h25NqlGcEsFkpo1p500QJSi6gmurWOA0N4BvAAmBb22sDB9bqnU4VFNWNrZ1PAY5vaPNQ25f275EjIiIiItojIzbLzxnASZI+YvtBSesDr7f9S6qRlAXA48BQ4MQlrPs04I+2/9QkbzjwKDBb0sbA52t5NwKrSPpkad97gdcAvy35ZwI/lfQHqk0J1gLeAvzB9nz6sO2YEXQP0h9rioiIiIgVX0Zslp9Tgd8B10iaC9zEC2tVzgamleOuktdvth+3fU0v2ccCOwCzqdbfXFYrt4AqmPko8ATw71Q7rfXk3wp8DPjvkv8PIL+PExERERGDnl683CJi6XR1dbm7u7vdzYiIiIiIFZykCba7GtMzYhMRERERER0vgU1ERERERHS8TEWLlijriO5tdztWAqOAGe1uxEog/Tww0s8DI/08cNLXAyP9PDAGcz9vYnu9xsTsihatcm+zuY7RWpK608/LX/p5YKSfB0b6eeCkrwdG+nlgdGI/ZypaRERERER0vAQ2ERERERHR8RLYRKuc2e4GrCTSzwMj/Tww0s8DI/08cNLXAyP9PDA6rp+zeUBERERERHS8jNhERERERETHS2ATEREREREdL4FNRERERER0vAQ28TxJ60j6haT5kiZL2ruX6yTpG5IeL8cpklTL307SBElPlv/drr9lVwat6GdJW0i6QtJjkmZKulLSv9TKHihpoaR5tWOXAXrEQaGF77NLHT39+MP+ll0ZtOh93qnhXZ1X+v19JT/vc//7+S2S/ihptqRJTfLHlfwnJd0jadeG/M9JeriU/7Gk1ZfTIw1KrehnSetLukjStJL/J0mvq+XvImlRw/t8wHJ+tEGlhe/zJElP1frxqob8vM/L/j6P7eXv8xElf3C9z7Zz5MA2wEXAJcAw4E3AbGDrJtcdCtwLvAwYA9wNHFbyVgMmA58DVgc+XT6vtriyK8vRon7eATgIWAdYFfgqcE+t7IHAje1+1k7v55JvYLNe7pH3uUX93HDtLsBcYK3yOe9z//t5B2A/4BBgUpP8m4FTgTWB9wGzgPVK3m7AI8DWwEuBa4Gvt/vZO62fgU2Bw4GNgCHlmhnAsJK/C/BQu5+10/u55E8Cdu3lHnmfW9TPDde+HFgIjCufB9X73PYG5BgcB7AW8AywRS3tvGZ/BICbgENqnw8Cbinn/wZMpey4V9IeBHZfXNmV4WhVPze5dh2qL+Drls8HshJ/EWxlP9N3YJP3efm8z2cDZ9c+533uZz/X8ndt/IICbAEsAIbX0m7ghX8wuRA4qZb3NuDhdj9/p/VzL9fNAV5bzndhEH0R7OR+pu/AJu/zcnifgWOBP9Y+D6r3OVPRoscWwELb/6il/ZXqXzoabV3yml23NfA3l7e9+FtDfm9lVwat6udGb6b6g/14Le3VkmZI+oekr0h6ybI0vMO0up+vL9MZfi5p3BKWXZG1/H2WNBR4P/CThqy8z/3r575sDTxge24v9TT7b7SBpHWX8D6dqlX9/CKqpmOvBtxXS15f0iOSJko6TdJay3KPDtPqfr5A1bTsqyT9ay097/NyeJ+B/fnnv8+D5n1OYBM9hlENUdbNBob349rZwDBJ6kc9fZVdGbSqn58n6WXA96mmPvS4HtgGWJ9qusl/AF9YppZ3llb2887AOOCVwDTg17Uv1XmfW/w+U72vM4Draml5n/vfz8tST7P/RizFfTpVq/r5eZLWpvpX8uNt99R9D7Ad1VS1twKvpZoeuLJoZT/vQ/X3eRPgj8CVkkb2cp+8z8v+Pu8EbAD8rJY8qN7nBDbRYx6wdkPa2lTz3Bd37drAvDJKs7h6+iq7MmhVPwMgaT3gKuAHti/qSbf9gO2JthfZvhM4gepfwVcWLetn29fbfsb2LOAzVPOLt+xP2ZVAS9/n4gDg3Hp63ucl6udlqafZfyOW4j6dqlX9DICkNYFfUU25PLkn3fbDtu8u7/NE4IvkfV6qfrb9J9tP2X6y9PEsYKde7pP3eRne5+IA4DLb83oSBtv7nMAmevwDeImkzWtp/wrc1eTau0pes+vuAl7V8K+wr2rI763syqBV/Yykl1IFNb+0feJi7mtgZRlFgBb2cxP1vsz73MJ+lrQx1Xztcxdz37zPS/eu3QVsKqn+L7aNf78b/xs90jDFdUXWqn6m7L51OdWa00MXc3ne59b97Vzc3+e8z0vZzyVQ/wD/PA2tUXvf53Yv8skxeA7gYqodNNYC3kjvu2ccBvydamej0VT/R9K4K9pnqHZF+yQv3hWt17Iry9Gifl4b+DNwei/32APYoJy/Evhf4Nh2P3sH9vPWVEPsQ6iG9b9NtbPXqosru7Icrejn2jXHANc3KZv3uf/9vAqwRumzyeV8tVr+LcA3S/p7efGuaLsDDwNbUe0i9QdWvl2klrmfqXaq/BVVYPOSJmV3AcZSffnbmGoK1dntfvYO7OexpexqJf0LwGO8sIlO3ucW/d0o1+xd8tSQPqje57Z3eo7Bc1DtrHU5MJ9qJ7O9S/pOVFNGeq4TcAowsxyn8OJd0F4NTACeAm4HXt3fsivD0Yp+phoOdqljXu0YW/K/SbXN5XzgAaqpO6u2+9k7sJ/fShXIzAceLfVt3p+yK8vRqr8b5Zp7gIOa3CPvc//7eZfyt6F+XFvLH0e17e1T5d3eteE+h5e+nkO1O93q7X72TutnqnV5Bp5s+Pu8U62Pp5b8KcD3qO1UtzIcLernrak2J5oPPA5cA3Q13Cfvcwv+bpRrrgS+2uQeg+p97vl/3hERERERER0ra2wiIiIiIqLjJbCJiIiIiIiOl8AmIiIiIiI6XgKbiIiIiIjoeAlsIiIiIiKi4yWwiYiIiIiIjpfAJiIiIiLsXnLIAAAAEklEQVQiOl4Cm4iIiIiI6Hj/H3Cm8MC9SmE1AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_fi(rf_feat_importance(m, xs_imp));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Removing redundant features"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing that makes this harder to interpret is that there seem to be some variables with very similar meanings. Let's try to remove redundent features. We can do this using \"*hierarchical cluster analysis*\", which find pairs of columns that are the most similar, and replaces them with the average of those columns. It does this recursively, until there's just one column. It plots a \"*dendrogram*\", which shows which columns were combined in which order, and how far away they are from each other.\n",
+ "\n",
+ "Here's how it looks:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cluster_columns(xs_imp)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: The most similar pairs are found by calculating the *rank correlation*, which means that all the values are replaced with their *rank* (i.e. first, second, third, etc within the column), and then the *correlation* is calculated. (Feel free to skip over this minor detail though, since it's not going to come up again in the book!)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the chart above, you can see pairs of columns that were extremely similar as the ones that were merged together early, far away from the \"root\" of the tree at the left. For example, the fields `ProductGroup` and `ProductGroupDesc` were merged quite early, as were `saleYear` and `saleElapsed`, and as were `fiModelDesc` and `fiBaseModel`. These might be so closely correlated they are practically synonyms for each other.\n",
+ "\n",
+ "Let's try removing some of these closely related features to see if the model can be simplified without impacting the accuracy. First, we create a function that quickly trains a random forest and returns the OOB score, by using a lower `max_samples` and higher `min_samples_leaf` . The *score* is a number returned by sklearn that is 1.0 for a perfect model, and 0.0 for a random model. (In statistics it's called *R^2*, although the details aren't important for this explanation). We don't need it to be very accurate--we're just going to use it to compare different models, based on removing some of the possibly redundent columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_oob(df):\n",
+ " m = RandomForestRegressor(n_estimators=40, min_samples_leaf=15,\n",
+ " max_samples=50000, max_features=0.5, n_jobs=-1, oob_score=True)\n",
+ " m.fit(df, y)\n",
+ " return m.oob_score_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's our baseline."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.8771039618198545"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "get_oob(xs_imp)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we try removing each variable one at a time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'saleYear': 0.8759666979317242,\n",
+ " 'saleElapsed': 0.8728423449081594,\n",
+ " 'ProductGroupDesc': 0.877877012281002,\n",
+ " 'ProductGroup': 0.8772503407182847,\n",
+ " 'fiModelDesc': 0.8756415073829513,\n",
+ " 'fiBaseModel': 0.8765165299438019,\n",
+ " 'Hydraulics_Flow': 0.8778545895742573,\n",
+ " 'Grouser_Tracks': 0.8773718142788077,\n",
+ " 'Coupler_System': 0.8778016988955392}"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "{c:get_oob(xs_imp.drop(c, axis=1)) for c in (\n",
+ " 'saleYear', 'saleElapsed', 'ProductGroupDesc','ProductGroup',\n",
+ " 'fiModelDesc', 'fiBaseModel',\n",
+ " 'Hydraulics_Flow','Grouser_Tracks', 'Coupler_System')}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's try dropping multiple variables. We'll drop one from each of the tightly aligned pairs we noticed above. Let's see what that does."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.8739605718147015"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "to_drop = ['saleYear', 'ProductGroupDesc', 'fiBaseModel', 'Grouser_Tracks']\n",
+ "get_oob(xs_imp.drop(to_drop, axis=1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking good! This is really not much worse than the model with all the fields. Let's create DataFrames without these columns, and save them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xs_final = xs_imp.drop(to_drop, axis=1)\n",
+ "valid_xs_final = valid_xs_imp.drop(to_drop, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(path/'xs_final.pkl').save(xs_final)\n",
+ "(path/'valid_xs_final.pkl').save(valid_xs_final)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can load them back later with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xs_final = (path/'xs_final.pkl').load()\n",
+ "valid_xs_final = (path/'valid_xs_final.pkl').load()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can check our RMSE again, to confirm it is still a similar accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.183263, 0.233846)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = rf(xs_final, y)\n",
+ "m_rmse(m, xs_final, y), m_rmse(m, valid_xs_final, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Partial dependence"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The two most important predictors are `ProductSize` and `YearMade`. We'd like to understand the relationship between these predictors and sale price. It's a good idea to first check the count of values per category (provided by the Pandas `value_counts` method), to see how common each category is:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "p = valid_xs_final['ProductSize'].value_counts(sort=False).plot.barh()\n",
+ "c = to.classes['ProductSize']\n",
+ "plt.yticks(range(len(c)), c);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The largrest group is `#na#`, which is the label fastai applies to missing values.\n",
+ "\n",
+ "Let's do the same thing for `YearMade`. However, since this is a numeric feature, we'll need to draw a histogram, which groups the year values into a few discrete bins:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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S5yAwM0ucg8DMLHEOAjOzxPnyURszjbqscPMNZzfk65o1K+8RmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpa4mgSBpJKkPZLK+eOZQu1CSVsk7Zb0gKQjCrUjJK3Ka1skXViL7TEzs+rVco9gYUS05o8TACTNAL4FfBxoA14FvlFocwvwel6bB9yatzEzszoZ65vOzQMeioifAEi6Cnha0iTg90A3MDMiysBjkn5AFhqLx3i7zMwsp4gYfSdSCZgBCHgG+EJElCQ9CDweEV8prFsG5pAFweMRMbFQ+xwwJyLOqeh/AbAAoK2t7ZR777131NsMUC6XaW1trUlfzWos5+DJ53eOSb9DmfWOw6pe15+B0c9Bo97nWmqbCNtfa/RWDG04n+1Kc+fOXRcRnf3VarVH8HfAL8gO85wPPCTpZKAVqPyU7AQmAb8bpPYWEbEMWAbQ2dkZXV1dNdnoUqlErfpqVmM5B/MbdRvqeV1Vr+vPwOjnoFHvcy0tmrWPm57c/+/KP5zP9nDUZOQR8R+Fl3dIugD4MFAGJlesPhnYRbZHMFDNzMzqZKwuHw2yw0Q9wOy+hZKOBcYDz+aPcZKmFdrNztuYmVmdjDoIJB0u6QxJEySNkzQP+DPgX4C7gXMkfUBSC/BFYGVE7IqI3cBK4IuSWiSdBnwEuGu022RmZtWrxaGhQ4DrgOlkx/03AudGxDMAkj5FFghHAo8Anyi0/TSwHPgN8BJwWUR4j8DMrI5GHQQRsQN4zyD17wLfHaD2MnDuaLfBzMxGzreYMDNLnIPAzCxxDgIzs8Q5CMzMEucgMDNLnIPAzCxxDgIzs8Q5CMzMEucgMDNL3P5/31UzG1LHKG4FvWjWvgPiVtI2ct4jMDNLnIPAzCxxDgIzs8Q5CMzMEucgMDNLnIPAzCxxvnzUrIZGcxmnWaN4j8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHENDwJJR0haJWm3pC2SLmz0NpmZpWR/uHz0FuB1oA04GXhY0vqI6GnsZpmZpaGhQSCpBegGZkZEGXhM0g+AjwOLx+JrFq/zruftdzffcHZdvo4N71p+34LZDBQRjfvi0ruBxyNiYmHZ54A5EXFOYdkCYEH+8gTgmRptwlTgxRr11axSn4PUxw+eA0hjDtoj4qj+Co0+NNQK7KxYthOYVFwQEcuAZbX+4pLWRkRnrfttJqnPQerjB88BeA4afbK4DEyuWDYZ2NWAbTEzS1Kjg+BZYJykaYVlswGfKDYzq5OGBkFE7AZWAl+U1CLpNOAjwF112oSaH25qQqnPQerjB88BJD4HDT1ZDNnvEQDLgb8AXgIWR8R3G7pRZmYJaXgQmJlZYzX6HIGZmTWYg8DMLHEHRBBIWihpraS9klZU1C6R9N+SypJWSzqmULtC0lOSdkl6TtIVFW07JK2R9KqkjZI+WKchDdtI56CwzqH5GH9dsfxkSevyOVgn6eQxHsqIjGb8kv5E0k/y+nZJny3UDvjPgKTxkr6Zj/1lSQ9Jekeh3jT3A8vHcnu+nbskPSHprEL99Px9fDV/X9sr2i6X9IqkbZIur+h7wLbN7oAIAuAF4Dqyk85vkjQH+BLZlUhHAM8B9xRXAS4CpgBnAgslnV+o3wM8ARwJfAH4J0n9/mbefmCkc9DnCuA3FW0PBR4EvkM2R3cAD+bL9zcjGr+kqcBq4Ftk7/MfA/9a6CKFz8BngfcDJwHHAL8Fbi7Ui/cDmwfcKmnG2Axh1MYBvwLmAIcBVwHfywN9KtlVileRzcNa4L5C26XANKAdmAtcKelMePNzMljb5hYRB8yD7D/BisLrrwG3FF4fAwRw3ADtvw7cnD8/HtgLTCrUfwp8qtHjrPUcAH8EPA2cBfy6sPxDwPPkFxXky34JnNnocdZq/GTfIO8aoK8kPgPArcBXC/WzgWfy5y1kIXB8oX4XcEOjxzmM+dhAdk+zBWS3tOlb3gK8BkzPXz8PfKhQvxa4N38+aNtmfxwoewQDUf4ovgaY+f9WlAR8gP/7ZbYZwP9ERPG3nNfny5tJNXNwM/B5sg920QxgQ+Sf/NwGmmsOhhr/+4CXJT0u6Tf5YZF35bVUPgO3A6dJOkbS28h+6v/nvHY88LuIeLbQvmnmQFIb2Rh6yLZ5fV8tst9j2gTMkDSFLCDXF5oXxzlg27Hc/no50IPgR8BfSzpJ0kTgarKfhN7Wz7pLyebj2/nrqu6D1AQGnQNJHwXGRcSqftoeCHMw1GfgncDfkB0eeRdvPWxyIIwfhp6DZ8n29J4HXgFOBL6Y15p2DiQdAtwN3BERGxl8LK2F15U1hmjb9A7oIIiIfwOuAb4PbAE2k93HqPKE6EKycwVnR8TefPEBcR+kweZA2W3Avwp8ZoDmTT8HVXwGXgNWRcR/RsQe4O+BP5V0GAfA+KGqObgVmEB2HqSF7Fh43x5BU86BpIPIDmG9DizMFw82lnLhdWVtqLZN74AOAoCIuCUipkXE28n+I4wDnuqrS7qY7G8fnB4RxYDoAY6VVEz8prwP0iBzMA3oAH4qaRvZN4A/yK+Y6CAb60n5YbM+J9FkczDEZ2AD2U/Hb66e/yvS+AxANqYVEfFy/oPQzcB78xOkTXc/sPzzejvZye3uiHgjL/WQbXvfei3AcUBPRPQCW4t13jrOAduO0TDqq9EnKWrxIPtQTwC+TPZTwITCsplk/6nfBZSALxXazQO2AScO0O/PyE60TQA+SnY1xVGNHm+t5iCvH114nEd25cnRwMHAoWQ/QX4WGE/2k9UW4NBGj7eGn4E/B3rJ/jreIcA/AD9N5TOQt/s2WTgcls/B54HnC/V7yQ6XtQCnkR0SmdHo8Q4yD9/M37fWiuVH5dvenc/JV4CfFeo3AI+SXSE3nSwYzqymbbM/Gr4BNXrjl5L9JFd8LAUOJ/uJbzfZN/wvAwcX2j0HvEG229f3+Gah3pH/p3mN7I/hfLDRY631HFT00UXhqqF82buBdfkc/Bfw7kaPtdbjBy4jOz7eCzwE/GFKnwGyQ0J3k10+/FvgMeC9hfoRwAN5+18CFzZ6rIPMQXs+7j0V/6/n5fUPAhvz97MEdBTajie79PYVYDtweUXfA7Zt9ofvNWRmlrgD/hyBmZkNzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiftfGkutOUh/ddgAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = valid_xs_final['YearMade'].hist()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Other than the special value 1950 which we used for coding missing year values, most of the data is after 1990.\n",
+ "\n",
+ "Now we're ready to look at *partial dependence plots*. Partial dependence plots try to answer the question: if a row varied on nothing other than the feature in question, how would it impact the dependent variable?\n",
+ "\n",
+ "For instance, how does `YearMade` impact sale price, all other things being equal?\n",
+ "\n",
+ "To answer this question, we can't just take the average sale price for each `YearMade`. The problem with that approach is that many other things vary from year to year as well, such as which products are sold, how many products have air-conditioning, inflation, and so forth. So merely averaging over all the auctions that have the same `YearMade` would also capture the effect of how every other field also changed along with `YearMade` and how that overall change affected price.\n",
+ "\n",
+ "Instead, what we do is replace every single value in the `YearMade` column with 1950, and then calculate the predicted sale price for every auction, and take the average over all auctions. Then we do the same for 1951, 1952, and so forth until our final year of 2011. This isolates the effect of only `YearMade` (even if it does so by averaging over some imagined records where we assign a `YearMade` value that might never actually exist alongside some other values). \n",
+ "\n",
+ "> A: If you are philosophically minded it is somewhat dizzying to contemplate the different kinds of hypotheticality that we are juggling to make this calculation. First, there's the fact that *every* prediction is hypothetical, because we are not noting empirical data. Second, there's the point that we're *not* merely interested in asking how would sale price change if we changed `YearMade` and everything else along with it. Rather, we're very specifically asking, how would sale price change in a hypothetical world where only `YearMade` changed. Phew! It is impressive that we can ask such questions. I recommend Judea Pearl's recent book on causality, *The Book of Why*, if you're interested in more deeply exploring formalisms for analyzing these subtleties.\n",
+ "\n",
+ "With these averages, we can then plot each of these years on the x-axis, versus each of the predictions on the Y axis. This, finally, is a partial dependence plot. Let's take a look:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.inspection import plot_partial_dependence\n",
+ "\n",
+ "fig,ax = plt.subplots(figsize=(12, 4))\n",
+ "plot_partial_dependence(m, valid_xs_final, ['YearMade','ProductSize'],\n",
+ " grid_resolution=20, ax=ax);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking first of all at the YearMade plot, and specifically at the section covering after 1990 (since as we noted this is where we have most of the data), we can see a nearly linear relationship between year and price. Remember that our dependent variable is after taking the logarithm, so this means that in practice there is a exponential increase in price. This is what we would expect: depreciation is generally recognised as being a multiplicative factor over time. So, for a given sale date, varying year made ought to show an exponential relationship with sale price.\n",
+ "\n",
+ "The `ProductSize` partial plot is a bit concerning. It shows that the final group, which we saw before is for missing values, has the lowest price. To use this insight in practice, we would want to find out *why* it's missing so often, and what that *means*. Missing values can sometimes be useful predictors--it entirely depends on what causes them to be missing. Sometimes, however, it can show *data leakage*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data leakage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the paper [Leakage in Data Mining: Formulation, Detection, and Avoidance](https://dl.acm.org/doi/10.1145/2020408.2020496) the authors introduce leakage as \n",
+ "\n",
+ "> : \"the introduction of information about the target of a data mining problem, which should not be legitimately available to mine from. A trivial example of leakage would be a model that uses the target itself as an input, thus concluding for example that 'it rains on rainy days'. In practice, the introduction of this illegitimate information is unintentional, and facilitated by the data collection, aggregation and preparation process.\"\n",
+ "\n",
+ "They give as an example\n",
+ "\n",
+ "> : \"a real-life business intelligence project at IBM where potential customers for certain products were identified, among other things, based on keywords found on their websites. This turned out to be leakage since the website content used for training had been sampled at the point in time where the potential customer has already become a customer, and where the website contained traces of the IBM products purchased, such as the word 'Websphere' (e.g. in a press release about the purchase or a specific product feature the client uses).\"\n",
+ "\n",
+ "Data leakage is subtle and can take many forms. In particular, missing values often represent data leakage.\n",
+ "\n",
+ "For instance, Jeremy competed in a Kaggle competition designed to predict which researchers would end up receiving research grants. The information was provided by a university, and included thousands of examples of research projects, along with information about the researchers involved, along with whether or not the grant was eventually accepted. The University hopes that they would be able to use models developed in this competition to help him rank which grant applications were most likely to succeed, so that they could prioritise their processing .\n",
+ "\n",
+ "Jeremy used a random forest to model the data, and then used feature importance to find out which features were most predictive. He noticed three surprising things:\n",
+ "\n",
+ "- The model was able to correctly predict who would receive grants over 95% of the time\n",
+ "- Apparently meaningless identifier columns were the most important predictors\n",
+ "- The columns day of week and day of year were also highly predictive; for instance, the vast majority of grant applications dated on a Sunday were accepted, and many grant applications were dated on January 1 and were also accepted.\n",
+ "\n",
+ "For the identifier columns, a partial dependence plots showed that when the information was missing the grant was almost always rejected. It turned out that much of this information, in practice at the University, was only filled in *after* a grant application was accepted. Often, for applications that were not accepted, it was just left blank. Therefore, this information was not something that was actually available at the time that the application was received, and would therefor not be available for a predictive model — it was data leakage.\n",
+ "\n",
+ "In the same way, the final processing of successful applications was often done automatically as a batch at the end of the week, or the end of the year. It was this final processing date which ended up in the data, so again, this information, while predictive, was not actually available at the time that the application was received.\n",
+ "\n",
+ "This example shows the most practical and simple approaches to identifying data leakage, which are to build a model, and then:\n",
+ "\n",
+ "- Check whether the accuracy of the model is *too good to be true*\n",
+ "- Look for important predictors which don't make sense in practice\n",
+ "- Look for partial dependence plot results which don't make sense in practice.\n",
+ "\n",
+ "Thinking back to our bear detector, this mirrors the advice that we also provided there — it is often a good idea to build a model first, and then do your data cleaning, rather than vice versa. The model can help you identify potentially problematic data issues."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Tree interpreter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "import warnings\n",
+ "warnings.simplefilter('ignore', FutureWarning)\n",
+ "\n",
+ "from treeinterpreter import treeinterpreter\n",
+ "from waterfall_chart import plot as waterfall"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At the start of this section, we said that we wanted to be able to answer five questions:\n",
+ "\n",
+ "- How confident are we in our projections using a particular row of data?\n",
+ "- For predicting with a particular row of data, what were the most important factors, and how did they influence that prediction?\n",
+ "- Which columns are the strongest predictors?\n",
+ "- Which columns are effectively redundant with each other, for purposes of prediction?\n",
+ "- How do predictions vary, as we vary these columns?\n",
+ "\n",
+ "We've handled three of these already--so just one to go, which is: \"For predicting with a particular row of data, what were the most important factors, and how did they influence that prediction?\" To answer this question, we need to use the `treeinterpreter` library. We'll also use the `waterfallcharts` library to draw the chart of the results.\n",
+ "\n",
+ " !pip install treeinterpreter\n",
+ " !pip install waterfallcharts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have already seen how to compute feature importances across the entire random forest. The basic idea was to look at the contribution of each variable towards improving the model, at each branch of every tree, and then to add up all of these contributions per variable.\n",
+ "\n",
+ "We can do exactly the same thing, but for just a single row of data. For instance, let's say we are looking at some particular item at auction. Our model might predict that this item will be very expensive, and we want to know why. So we take that one row of data, and put it through the first decision tree, looking to see what split is used at each point throughout the tree. For each split, we see what the increase or decrease in the addiction is, compared to the parent node of the tree. We do this for every tree, and add up the total change in importance by split variable.\n",
+ "\n",
+ "For instance, let's pick the first few rows of our validation set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "row = valid_xs_final.iloc[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can pass these to `treeinterpreter`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prediction,bias,contributions = treeinterpreter.predict(m, row.values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`prediction` is simply the prediction that the random forest makes. `bias` is the prediction based on simply taking the mean of the dependent variable (i.e. the *model* that is the root of every tree). `contributions` is the most interesting bit--it tells us the total change in predicition due to each of the independent variables. Therefore, the sum of `contributions` plus `bias` must equal the `prediction`, for each row. Let's look just at the first row:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([9.98234598]), 10.104309759725059, -0.12196378442186026)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "prediction[0], bias[0], contributions[0].sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The clearest way to display the contributions is with a *waterfall plot*. This shows how each positive and negative contribution from all the independent variables sum up to create the final prediction, which is the right-hand column labeled \"net\" here:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "waterfall(valid_xs_final.columns, contributions[0], threshold=0.08, \n",
+ " rotation_value=45,formatting='{:,.3f}');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This kind of information is most useful in production, rather than during model development. You can use it to provide useful information to users of your data product about the underlying reasoning behind the predictions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extrapolation and neural networks"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The extrapolation problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "np.random.seed(42)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's consider the simple task of making predictions from 40 data points showing a slightly noisy linear relationship:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x_lin = torch.linspace(0,20, steps=40)\n",
+ "y_lin = x_lin + torch.randn_like(x_lin)\n",
+ "plt.scatter(x_lin, y_lin);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although we only have a single independent variable, sklearn expects a matrix of independent variables, not a single vector. So we have to turn our vector into a matrix with one column. In other words, we have to change the *shape* from `[40]` to `[40,1]`. One way to do that is with the `unsqueeze` method, which adds a new unit axis to a tensor at the requested dimension:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([40]), torch.Size([40, 1]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xs_lin = x_lin.unsqueeze(1)\n",
+ "x_lin.shape,xs_lin.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A more flexible approach is to slice an array or tensor with the special value `None`, which introduces an additional unit axis at that location:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([40, 1])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x_lin[:,None].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create a random forest for this data. We'll use only the first 30 rows to train the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m_lin = RandomForestRegressor().fit(xs_lin[:30],y_lin[:30])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and we test the model on the full dataset. The blue dots are the training data, and the red dots are the predictions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(x_lin, y_lin, 20)\n",
+ "plt.scatter(x_lin, m_lin.predict(xs_lin), color='red', alpha=0.5);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have a big problem! Our predictions outside of the domain that our training data covered are all too low. Have a think about why this is…\n",
+ "\n",
+ "Remember, a random forest is just the average of the predictions of a number of trees. And a tree simply predicts the average value of the rows in a leaf. Therefore, a tree and a random forest can never predict values outside of the range of the training data. This is particularly problematic for data where there is a trend over time, such as inflation, and you wish to make predictions for a future time.. Your predictions will be systematically to low.\n",
+ "\n",
+ "But the problem is actually more general than just time variables. Random forests are not able to extrapolate outside of the types of data you have seen, in a more general sense. It can only ever average previously seen observations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Finding out of domain data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sometimes it is hard to even know whether your test set is distributed in the same way as your training data or, if it is different, then what columns reflect that difference. There's actually a nice easy way to figure this out, which is to use a random forest!\n",
+ "\n",
+ "But in this case we don't use a random forest to predict our actual dependent variable. Instead we try to predict whether a row is in the validation set, or the training set. To see this in action, let's combine our training and validation sets together, create a dependent variable which represents which dataset each row comes from, build a random forest using that data, and get its feature importance:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " cols imp\n",
+ "5 saleElapsed 0.859446\n",
+ "9 SalesID 0.119325\n",
+ "13 MachineID 0.014259\n",
+ "0 YearMade 0.001793\n",
+ "8 fiModelDesc 0.001740\n",
+ "11 Enclosure 0.000657"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_dom = pd.concat([xs_final, valid_xs_final])\n",
+ "is_valid = np.array([0]*len(xs_final) + [1]*len(valid_xs_final))\n",
+ "\n",
+ "m = rf(df_dom, is_valid)\n",
+ "rf_feat_importance(m, df_dom)[:6]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This shows that there are three columns that are very different between training and validation set: `saleElapsed`, `SalesID`, and `MachineID`. `saleElapsed` is fairly obvious, since it's the number of days between the start of the dataset and each row, so it directly encodes the date. `SalesID` suggests that identifiers for auction sales might increment over time. `MachineID` suggests something similar might be happening for individual items sold in those auctions.\n",
+ "\n",
+ "We'll try training the original RF model, removing each of these in turn, and also checking the baseline model RMSE:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "orig 0.232795\n",
+ "SalesID 0.23109\n",
+ "saleElapsed 0.236221\n",
+ "MachineID 0.233492\n"
+ ]
+ }
+ ],
+ "source": [
+ "m = rf(xs_final, y)\n",
+ "print('orig', m_rmse(m, valid_xs_final, valid_y))\n",
+ "\n",
+ "for c in ('SalesID','saleElapsed','MachineID'):\n",
+ " m = rf(xs_final.drop(c,axis=1), y)\n",
+ " print(c, m_rmse(m, valid_xs_final.drop(c,axis=1), valid_y))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It looks like we should be able to remove `SalesID` and `MachineID` without losing any accuracy; let's check:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.231307"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "time_vars = ['SalesID','MachineID']\n",
+ "xs_final_time = xs_final.drop(time_vars, axis=1)\n",
+ "valid_xs_time = valid_xs_final.drop(time_vars, axis=1)\n",
+ "\n",
+ "m = rf(xs_final_time, y)\n",
+ "m_rmse(m, valid_xs_time, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Removing these variables has slightly improved the model's accuracy; but more importantly, it should make it more resilient over time, and easier to maintain and understand. We recommend that for all datasets you try building a model where your dependent variable is `is_valid`, like the above. It can often uncover subtle *domain shift* issues that you may otherwise miss.\n",
+ "\n",
+ "One thing that might help in our case is to simply avoid using old data. Often, old data shows relationships that just aren't valid any more. Let's try just using the most recent few years of the data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "| \n", + " | cols | \n", + "imp | \n", + "
|---|---|---|
| 5 | \n", + "saleElapsed | \n", + "0.859446 | \n", + "
| 9 | \n", + "SalesID | \n", + "0.119325 | \n", + "
| 13 | \n", + "MachineID | \n", + "0.014259 | \n", + "
| 0 | \n", + "YearMade | \n", + "0.001793 | \n", + "
| 8 | \n", + "fiModelDesc | \n", + "0.001740 | \n", + "
| 11 | \n", + "Enclosure | \n", + "0.000657 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "xs['saleYear'].hist();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filt = xs['saleYear']>2004\n",
+ "xs_filt = xs_final_time[filt]\n",
+ "y_filt = y[filt]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's the result of training on this subset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.17768, 0.230631)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = rf(xs_filt, y_filt)\n",
+ "m_rmse(m, xs_filt, y_filt), m_rmse(m, valid_xs_time, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's a tiny bit better, which shows that you shouldn't always just use your entire dataset; sometimes a subset can be better."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Using a neural network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use the same approach to build a neural network model. Let's first replicate the steps we took to set up the `TabularPandas` object:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_nn = pd.read_csv(path/'TrainAndValid.csv', low_memory=False)\n",
+ "df_nn['ProductSize'] = df_nn['ProductSize'].astype('category')\n",
+ "df_nn['ProductSize'].cat.set_categories(sizes, ordered=True, inplace=True)\n",
+ "df_nn[dep_var] = np.log(df_nn[dep_var])\n",
+ "df_nn = add_datepart(df_nn, 'saledate')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can leverage the work we did to trim unwanted column in the random forest, by using the same set of columns for our neural network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_nn_final = df_nn[list(xs_final_time.columns) + [dep_var]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Categorical columns are handled very differently in neural networks, compared to decision tree approaches. As we have seen in <>, a great way to handle categorical variables is by using embeddings. In order to create embeddings, fastai needs to know which columns should be treated as categorical variables. It does this by comparing the number of distinct levels in the variable (this is known as the *cardinality* of the variable) to the `max_card` parameter. Anything lower than this is going to be treated as a categorical variable by fastai. Embedding sizes larger than 10,000 should generally only be used after you've tested whether there are better ways to group the variable, so we'll use 9000 as our `max_card`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cont_nn,cat_nn = cont_cat_split(df_nn_final, max_card=9000, dep_var=dep_var)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "However, one variable that we absolutely do not want to treat as categorical is the saleElapsed variable. A categorical variable cannot, by definition, extrapolate outside the range of values that it has seen. But we want to be able to predict auction sale prices in the future. Therefore, we need to make this a continuous variable:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cont_nn.append('saleElapsed')\n",
+ "cat_nn.remove('saleElapsed')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's take a look at the cardinality of each of our categorical variables that we have chosen so far:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "YearMade 73\n",
+ "ProductSize 6\n",
+ "Coupler_System 2\n",
+ "fiProductClassDesc 74\n",
+ "ModelID 5281\n",
+ "Hydraulics_Flow 3\n",
+ "fiSecondaryDesc 177\n",
+ "fiModelDesc 5059\n",
+ "ProductGroup 6\n",
+ "Enclosure 6\n",
+ "fiModelDescriptor 140\n",
+ "Drive_System 4\n",
+ "Hydraulics 12\n",
+ "Tire_Size 17\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_nn_final[cat_nn].nunique()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fact that there are two variables pertaining to the \"model\" of the equipment, both with similar very high cardinalities, suggests that they may contain similar, redundant information. Note that we would not necessarily see this in the dendrogram, since that relies on similar variables being sorted in the same order (that is, they need to have similarly named levels). Having a column with 5000 levels means needing a number 5000 columns in our embedding matrix, so this would be nice to avoid if possible. Let's see what the impact of removing one of these model columns has on the random forest:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.176706, 0.230642)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xs_filt2 = xs_filt.drop('fiModelDescriptor', axis=1)\n",
+ "valid_xs_time2 = valid_xs_time.drop('fiModelDescriptor', axis=1)\n",
+ "m2 = rf(xs_filt2, y_filt)\n",
+ "m_rmse(m, xs_filt2, y_filt), m_rmse(m2, valid_xs_time2, valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There's minimal impact, so we will remove it as a predictor for our neural network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cat_nn.remove('fiModelDescriptor')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create our `TabularPandas` object in the same way as when we created our random forest, with one very important addition: normalisation. A random forest does not need any normalisation--the tree building procedure cares only about the order of values in a variable, not at all about how they are scaled. But as we have seen, a neural network definitely does care about this. Therefore, we add the `Normalize` processor when we build our `TabularPandas` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "procs_nn = [Categorify, FillMissing, Normalize]\n",
+ "to_nn = TabularPandas(df_nn_final, procs_nn, cat_nn, cont_nn,\n",
+ " splits=splits, y_names=dep_var)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tabular models and data don't generally require much GPU RAM, so we can use larger batch sizes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dls = to_nn.dataloaders(1024)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we've discussed, it's a good idea to set `y_range` for regression models, so let's find the min and max of our dependent variable:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(8.465899897028686, 11.863582336583399)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y = to_nn.train.y\n",
+ "y.min(),y.max()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create the `Learner` to create this tabular model. As usual, we use the application-specific learner function, to take advantage of its application-customized defaults. We set the loss function to MSE, since that's what this competition uses.\n",
+ "\n",
+ "By default, for tabular data fastai creates a neural network with two hidden layers, with 200 and 100 activations each, respectively. This works quite well for small datasets, but here we've got quite a large dataset, so we increase the layer sizes to 500 and 250."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.tabular.all import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = tabular_learner(dls, y_range=(8,12), layers=[500,250],\n",
+ " n_out=1, loss_func=F.mse_loss)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(0.005754399299621582, 0.0002754228771664202)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.lr_find()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There's no need to use `fine_tune`, so we'll train with 1-cycle for a few epochs and see how it looks..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(5, 1e-2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use our `r_mse` function to compare to the random forest result we got earlier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "0.2258"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "preds,targs = learn.get_preds()\n",
+ "r_mse(preds,targs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's quite a bit better than the random forest (although it took longer to train, and it's more fussy about hyperparameter tuning).\n",
+ "\n",
+ "Before we move on, let's save our model in case we want to come back to it again later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.save('nn')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### fastai's Tabular classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In fastai, a tabular model is simply a model which takes columns of continuous or categorical data, and predicts a category (a classification model) or a continuous value (a regression model). Categorical independent variables are passed through an embedding, and concatenated, as we saw in the neural net we used for collaborative filtering, and then continuous variables are concatenated as well.\n",
+ "\n",
+ "The model created in `tabular_learner` is an object of class `TabularModel`. Take a look at the source for `tabular_learner` now (remember, that's `tabular_learner??` in jupyter). You'll see that like `collab_learner`, it first calls `get_emb_sz` to calculate appropriate embedding sizes (which you can override by using the `emb_szs` parameter, which is a dictionary containing any column names you want to set sizes for manually), and it sets a few other defaults. Other than that, it just creates the `TabularModel`, and passes that to `TabularLearner` (and note that `TabularLearner` is identical to `Learner`, except for a customized `predict` method).\n",
+ "\n",
+ "That means that really all the work is happening in `TabularModel`, so take a look at the source for that now. With the exception of the `BatchNorm1d` and `Dropout` layers (which we'll be learning about shortly) you now have the knowledge required to understand this whole class. Take a look at the discussion of `EmbeddingNN` at the end of the last chapter. Recall that it passed `n_cont=0` to `TabularModel`. We now can see why that was: because there are zero continuous variables (in fastai the `n_` prefix means \"number of\", and `cont` is an abbreviation for \"continuous\")."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Ensembling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Think back to the original reasoning behind why random forests work so well: each tree has errors, but those errors are not correlated with each other, so the average of those errors should tend towards zero once there are enough trees. Similar reasoning could be used to consider averaging the predictions of models trained using different algorithms.\n",
+ "\n",
+ "In our case, we have two very different models, trained using very different algorithms: a random forest, and a neural network. It would be reasonable to expect that the kinds of errors that each one makes would be quite different. Therefore, we might expect that the average of their predictions would be better than either one's individual predictions.\n",
+ "\n",
+ "As we mentioned earlier in this chapter, the approach of combining multiple models' predictions together is called *ensembling*. A random forest is itself an ensemble. But we can then include a random forest in *another* ensemble--an ensemble of the random forest and the neural network! Whilst it is not going to make the difference between a successful and unsuccessful modelling process, it can certainly add a nice little boost to any models that you have built.\n",
+ "\n",
+ "One minor issue we have to be aware of is that our PyTorch model and our sklearn model create data of different types--PyTorch gives us a rank 2 tensor (i.e a column matrix), whereas numpy gives us a rank 1 array (a vector). `squeeze()` removes any unit axes from a tensor, and `to_np` converts it into a numpy array."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rf_preds = m.predict(valid_xs_time)\n",
+ "ens_preds = (to_np(preds.squeeze()) + rf_preds) /2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This gives us a better result than either model achieved on its own:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.22291"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "r_mse(ens_preds,valid_y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In fact, this result is better than any score shown on the Kaggle leaderboard. This is not directly comparable, however, because the Kaggle leaderboard uses a separate dataset that we do not have access to. Kaggle does not allow us to submit to this old competition, to find out how we would have gone, so we have no way to directly compare. But our results certainly look very encouraging!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Boosting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far our approach to ensembling has been to use *bagging*, which involves combining many models together by averaging them, where each model is trained on a different data subset. When this is applied to decision trees, this is called a *random forest*.\n",
+ "\n",
+ "There is another important approach to ensembling, called *boosting*, where we add models, instead of averaging them. Here is how it works:\n",
+ "\n",
+ "- Train a small model which under fits your dataset\n",
+ "- Calculate the predictions in the training set for this model\n",
+ "- Subtract the predictions from the targets; these are called the \"residuals\", and represent the error for each point in the training set\n",
+ "- Go back to step one, but instead of using the original targets, use the residuals as the target for the training\n",
+ "- Continue doing this until you reach some stopping criterion, such as a maximum number of trees, or you observe your validation set error getting worse.\n",
+ "\n",
+ "Using this approach, each new tree will be attempting to fit the error of all of the previous trees combined. Because we are continually creating new residuals, by subtracting the predictions of each new tree from the residuals from the previous tree, the residuals will get smaller and smaller.\n",
+ "\n",
+ "To make predictions with an ensemble of boosted trees, we calculate the predictions from each tree, and then add them all together. There are many models following this basic approach, and many names for the same models! *Gradient boosting machines* (GBMs) and *gradient boosted decision trees* (GBDTs) are the terms you're most likely to come across, or you may see the names of specific libraries implementing these; at the time of writing, *XGBoost* is the most popular.\n",
+ "\n",
+ "Note that, unlike random forests, there is nothing to stop us from overfitting. Using more trees in a random forest does not lead to overfitting, because each tree is independent of the others. But in a boosted ensemble, the more trees you have, the better the training error becomes, and eventually you will see overfitting on the validation set.\n",
+ "\n",
+ "We are not going to go into details as to how to train a gradient boosted tree ensemble here, because the field is moving rapidly, and any guidance we give will almost certainly be outdated by the time you read this! As we write this, sklearn has just added a `HistGradientBoostingRegressor` class, which provides excellent performance. There are many hyperparameters to tweak for this class, and for all gradient boosted tree methods we have seen. Unlike random forests, gradient boosted trees are extremely sensitive to the choices of these hyperparameters. So in practice, most people will use a loop which tries a range of different hyperparameters, to find which works best."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Combining embeddings with other methods"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The abstract of the entity embedding paper states: \"*the embeddings obtained from the trained neural network boost the performance of all tested machine learning methods considerably when used as the input features instead*\". It includes this very interesting table:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Embeddings](\"images/att_00054.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is showing the mean average percent error (MAPE) compared amongst four different modelling techniques, three of which we have already seen, along with \"KNN\" (K nearest neighbours), which is a very simple baseline method. The first numeric column contains the results using just the methods on the data provided in the competition; the second column shows what happens if you first train a neural network with categorical embeddings, and then use those categorical embeddings instead of the raw categorical columns in the model. As you see, in every case, the models are dramatically improved by using the embeddings, instead of the raw category.\n",
+ "\n",
+ "This is a really important result, because it shows that you can get much of the performance improvement of a neural network, without actually having to use a neural network at all at inference time. You could just use an embedding, which is literally just an array lookup, along with a small decision tree ensemble.\n",
+ "\n",
+ "These embeddings need not even be necessarily learned separately for each model or task in an organisation. Instead, once a set of embeddings are learned for some column for some task, they could be stored in a central place, and reused across multiple models. In fact, we know from private communication with other practitioners at large companies that this is already happening in many places."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Our advice for tabular modeling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have two approaches to tabular modelling: decision tree ensembles, and neural networks. And we have mentioned two different decision tree ensembles: random forests, and gradient boosting machines. Each is very effective, but each also has compromises:\n",
+ "\n",
+ "**Random forests** are the easiest to train, because they are extremely resilient to hyperparameter choices, and require very little preprocessing. They are very fast to train, and should not overfit, if you have enough trees. But, they can be a little less accurate, especially if extrapolation is required, such as predicting future time periods\n",
+ "\n",
+ "**Gradient boosting machines** in theory are just as fast to train as random forests, but in practice you will have to try lots of different hyperparameters. They can overfit. But they are often a little bit more accurate than random forests.\n",
+ "\n",
+ "**Neural networks** take the longest time to train, and require extra preprocessing such as normalisation; this normalisation needs to be used at inference time as well. They can provide great results, and extrapolate well, but only if you are careful with your hyperparameters, and are careful to avoid overfitting.\n",
+ "\n",
+ "We suggest starting your analysis with a random forest. This will give you a strong baseline, and you can be confident that it's a reasonable starting point. You can then use that model for feature selection and partial dependence analysis, to get a better understanding of your data.\n",
+ "\n",
+ "From that foundation, you can try neural nets and GBMs, and if they give you significantly better results on your validation set in a reasonable amount of time, you can use them. If decision tree ensembles are working well for you, try adding the embeddings for the categorical variables to the data, and see if that helps your decision trees learn better."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What is a continuous variable?\n",
+ "1. What is a categorical variable?\n",
+ "1. Provide 2 of the words that are used for the possible values of a categorical variable.\n",
+ "1. What is a \"dense layer\"?\n",
+ "1. How do entity embeddings reduce memory usage and speed up neural networks?\n",
+ "1. What kind of datasets are entity embeddings especially useful for?\n",
+ "1. What are the two main families of machine learning algorithms?\n",
+ "1. Why do some categorical columns need a special ordering in their classes? How do you do this in pandas?\n",
+ "1. Summarize what a decision tree algorithm does.\n",
+ "1. Why is a date different from a regular categorical or continuous variable, and how can you preprocess it to allow it to be used in a model?\n",
+ "1. Should you pick a random validation set in the bulldozer competition? If no, what kind of validation set should you pick?\n",
+ "1. What is pickle and what is it useful for?\n",
+ "1. How are `mse`, `samples`, and `values` calculated in the decision tree drawn in this chapter?\n",
+ "1. How do we deal with outliers, before building a decision tree?\n",
+ "1. How do we handle categorical variables in a decision tree?\n",
+ "1. What is bagging?\n",
+ "1. What is the difference between `max_samples` and `max_features` when creating a random forest?\n",
+ "1. If you increase `n_estimators` to a very high value, can that lead to overfitting? Why or why not?\n",
+ "1. What is *out of bag error*?\n",
+ "1. Make a list of reasons why a model's validation set error might be worse than the OOB error. How could you test your hypotheses?\n",
+ "1. How can you answer each of these things with a random forest? How do they work?:\n",
+ " - How confident are we in our projections using a particular row of data?\n",
+ " - For predicting with a particular row of data, what were the most important factors, and how did they influence that prediction?\n",
+ " - Which columns are the strongest predictors?\n",
+ " - How do predictions vary, as we vary these columns?\n",
+ "1. What's the purpose of removing unimportant variables?\n",
+ "1. What's a good type of plot for showing tree interpreter results?\n",
+ "1. What is the *extrapolation problem*?\n",
+ "1. How can you tell if your test or validation set is distributed in a different way to your training set?\n",
+ "1. Why do we make `saleElapsed` a continuous variable, even although it has less than 9000 distinct values?\n",
+ "1. What is boosting?\n",
+ "1. How could we use embeddings with a random forest? Would we expect this to help?\n",
+ "1. Why might we not always use a neural net for tabular modeling?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Pick a competition on Kaggle with tabular data (current or past) and try to adapt the techniques seen in this chapter to get the best possible results. Compare yourself to the private leaderboard.\n",
+ "1. Implement the decision tree algorithm in this chapter from scratch yourself, and try it on this dataset.\n",
+ "1. Use the embeddings from the neural net in this chapter in a random forest, and see if you can improve on the random forest results we saw.\n",
+ "1. Explain what each line of the source of `TabularModel` does (with the exception of the `BatchNorm1d` and `Dropout` layers)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/10_nlp.ipynb b/10_nlp.ipynb
new file mode 100644
index 000000000..58f04e194
--- /dev/null
+++ b/10_nlp.ipynb
@@ -0,0 +1,2360 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *\n",
+ "from IPython.display import display,HTML"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_nlp]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NLP deep dive: RNNs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In <> we saw that deep learning can be used to get great results with natural language datasets. Our example relied on using a pretrained language model and fine-tuning it to classify those reviews. One thing is a bit different from the transfer learning we have in computer vision: the pretrained model was not trained on the same task as the model we used to classify reviews.\n",
+ "\n",
+ "What we call a language model is a model that has been trained to guess what the next word in a text is (having read the ones before). This kind of task is called *self-supervised learning*: we do not need to give labels to our model, just feed it lots and lots of texts. It has a process to automatically get labels from the data, and this task isn't trivial: to properly guess the next word in a sentence, the model will have to get an understanding of the English-- or other--language. Self-supervised learning can also be used in other domains; for instance, see [Self-supervised learning and computer vision](https://www.fast.ai/2020/01/13/self_supervised/) for an introduction to vision applications. Self-supervised learning is not usually used for the model that is trained directly, but instead is used for pre-training a model used for transfer learning."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Self-supervised learning: Training a model using labels that are embedded in the independent variable, rather than requiring external labels. For instance, training a model to predict the next word in a text."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The language model we used in <> to classify IMDb reviews was pretrained on Wikipedia. We got great results by directly fine-tuning this language model to a movie review classifier, but with one extra step, we can do even better: the Wikipedia English is slightly different from the IMDb English. So instead of jumping directly to the classifier, we could finetune our pretrained language model to the IMDb corpus and *then* use that as the base for our classifier.\n",
+ "\n",
+ "Even if our language model knows the basics of the language we are using in the task (e.g., our pretrained model is in English), it helps to get used to the style of the corpus we are targetting. It may be more informal language, or more technical, with new words to learn or different ways of composing sentences. In the case of IMDb, there will be lots of names of movie directors and actors, and often a less formal style of language that seen in Wikipedia.\n",
+ "\n",
+ "We saw that with fastai, we can download a pre-trained language model for English, and use it to get state-of-the-art results for NLP classification. (We expect pre-trained models in many more languages to be available soon — they might well be available by the time you are reading this book, in fact.) So, why are we learning how to train a language model in detail?\n",
+ "\n",
+ "One reason, of course, is that it is helpful to understand the foundations of the models that you are using. But there is another very practical reason, which is that you get even better results if you fine tune the (sequence-based) language model prior to fine tuning the classification model. For instance, for the IMDb sentiment analysis task, the dataset includes 50,000 additional movie reviews that do not have any positive or negative labels attached. So that is 100,000 movie reviews altogether (since there are also 25,000 labelled reviews in the training set, and 25,000 in the validation set). We can use all 100,000 of these reviews to fine tune the pretrained language model — this will result in a language model that is particularly good at predicting the next word of a movie review. In contrast, the pretrained model was trained only on Wikipedia articles.\n",
+ "\n",
+ "The [ULMFiT paper](https://arxiv.org/abs/1801.06146) showed that this extra stage of language model fine tuning, prior to transfer learning to a classification task, resulted in significantly better predictions. Using this approach, we have three stages for transfer learning in NLP, as summarised in this figure:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Diagram](\"images/att_00027.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Language model fine tuning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A *language model* is a model that learns to predict the next word of a sentence. Have a think about how you would turn this language modelling problem into a neural network, given what you have learned so far. We'll be able to use concepts that we've seen in the last two chapters.\n",
+ "\n",
+ "It's not at all obvious how we're going to use what we've learned so far to build a language model. Sentences can be different lengths, and documents can be very long. So, how can we predict the next word of a sentence using a neural network? Let's find out!\n",
+ "\n",
+ "We've already seen how categorical variables can be used as independent variables for a neural network. The approach we took for a single categorical variable was to:\n",
+ "\n",
+ "1. Make a list of all possible levels of that categorical variable (let us call this list the *vocab*)\n",
+ "1. Replace each level with its index in the vocab\n",
+ "1. Create an embedding matrix for this containing a row for each level (i..e, for each item of the vocab)\n",
+ "1. Use this embedding matrix as the first layer of a neural network. (A dedicated embedding matrix can take as inputs the raw vocab indexes created in step two; this is equivalent to, but faster and more efficient, than a matrix which takes as input one-hot encoded vectors representing the indexes)\n",
+ "\n",
+ "We can do nearly the same thing with text! What is new is the idea of a sequence. First we concatenate all of the documents in our dataset into one big long string and split it into words, giving us a very long list of words. Our independent variable will be the sequence of words starting with the first word in our very long list and ending with the second last, and our dependent variable would be the sequence of words starting with the second word and ending with the last word. \n",
+ "\n",
+ "When creating our vocab, we will have very common words that will probably be in the vocabulary of our pretrained model, but we will also have new words specific to our corpus (cinematographic terms, or actor names for instance). Our embedding matrix will be built accordingly: for words that are in the vocabulary of our pretrained model, we will take the corresponding row in the embedding matrix of this pretrained model; but for new words, we won't have anything, so we will just initialize the corresponding row with a random vector."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each of the steps necessary to create a language model has jargon associated with it from the world of natural language processing, and fastai and PyTorch classes available to help. The steps are:\n",
+ "\n",
+ "- **Tokenization**: convert the text into a list of words (or characters, or substrings, depending on the granularity of your model)\n",
+ "- **Numericalization**: make a list of all of the unique words which appear (the vocab), and convert each word into a number, by looking up its index in the vocab\n",
+ "- **Language model data loader** creation: fastai provides an `LMDataLoader` class which automatically handles creating a dependent variable which is offset from the independent variable buy one token. It also handles some important details, such as how to shuffle the training data in such a way that the dependent and independent variables maintain their structure as required\n",
+ "- **Language model** creation: we need a special kind of model which does something we haven't seen before: handles input lists which could be arbitrarily big or small. There are a number of ways to do this; in this chapter we will be using a *recurrent neural network*. We will get to the details of this in the <>, but for now, you can think of it as just another deep neural network.\n",
+ "\n",
+ "Let's take a look at how each step works in detail."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Preprocessing text with fastai"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Tokenization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "When we said, *convert the text into a list of words*, we left out a lot of details. For instance, what do we do with punctuation? How do we deal with a word like \"don't\"? Is it one word, or two? What about long medical or chemical words? Should they be split into their separate pieces of meaning? How about hyphenated words? What about languages like German and Poland where we can create really long words from many, many pieces? What about languages like Japanese and Chinese which don't use bases at all, and don't really have a well-defined idea of *word*?\n",
+ "\n",
+ "Because there is no one correct answer to these questions, there is no one approach to tokenization. Each element of the list created by the tokenisation process is called a *token*. There are three main approaches:\n",
+ "\n",
+ "- **Word-based**: split a sentence on spaces, as well as applying language specific rules to try to separate parts of meaning, even when there are no spaces, such as turning \"don't\" into \"do n't\". Generally, punctuation marks are also split into separate tokens\n",
+ "- **Subword based**: split words into smaller parts, based on the most commonly occurring substrings. For instance, \"occasion\" might be tokeniser as \"o c ca sion\"\n",
+ "- **Character-based**: split a sentence into its individual characters.\n",
+ "\n",
+ "We'll be looking at word and subword tokenization here, and we'll leave character-based tokenization for you to implement in the questionnaire at the end of this chapter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "> jargon: token: one element of a list created by the tokenisation process. It could be a word, part of a word (a _subword_), or a single character."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Word tokenization with fastai"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Rather than providing its own tokenizers, fastai instead provides a consistent interface to a range of tokenisers in external libraries. Tokenization is an active field of research, and new and improved tokenizers are coming out all the time, so the defaults that fastai uses change too. However, the API and options shouldn't change too much, since fastai tries to maintain a consistent API even as the underlying technology changes.\n",
+ "\n",
+ "Let's try it out with the IMDb dataset that we used in <>:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "from fastai2.text.all import *\n",
+ "path = untar_data(URLs.IMDB)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "We'll need to grab the text files in order to try out a tokenizer. Just like `get_image_files`, which we've used many times already, gets all the image files in a path, `get_text_files` gets all the text files in a path. We can also optionally pass `folders` to restrict the search to a particular list of subfolders:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "files = get_text_files(path, folders = ['train', 'test', 'unsup'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Here's a review that we'll tokenize (we'll just print the start of it here to save space):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'This movie, which I just discovered at the video store, has apparently sit '"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "txt = files[0].open().read(); txt[:75]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "As we write this book, the default *English word tokenizer* for fastai uses a library called *spaCy*. This uses a sophisticated rules engine that has special rules for URLs, individual special English words, and much more. Rather than directly using `SpacyTokenizer`, however, we'll use `WordTokenizer`, since that will always point to fastai's current default word tokenizer (which may not always be Spacy, depending when you're reading this).\n",
+ "\n",
+ "Let's try it out. We'll use fastai's `coll_repr(collection,n)` function to display the results; this displays the first `n` items of `collection`, along with the full size--it's what `L` uses by default. Not that fastai's tokenizers take a collection of documents to tokenize, so we have to wrap `txt` in a list:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(#201) ['This','movie',',','which','I','just','discovered','at','the','video','store',',','has','apparently','sit','around','for','a','couple','of','years','without','a','distributor','.','It',\"'s\",'easy','to','see'...]\n"
+ ]
+ }
+ ],
+ "source": [
+ "spacy = WordTokenizer()\n",
+ "toks = first(spacy([txt]))\n",
+ "print(coll_repr(toks, 30))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "As you see, spaCy has mainly just separated out the words and punctuation. But it does something else here too: it has split \"it's\" into \"it\" and \"'s\". That makes intuitive sense--these are separate words, really. Tokenization is a surprisingly subtle task, when you think about all the little details that have to be handled. spaCy handles these for us, for instance, here we see that \".\" is separated when it terminates a sentence, but not in an acronym or number:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#9) ['The','U.S.','dollar','$','1','is','$','1.00','.']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "first(spacy(['The U.S. dollar $1 is $1.00.']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "fastai then adds some additional functionality to the tokenization process with the `Tokenizer` class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(#228) ['xxbos','xxmaj','this','movie',',','which','i','just','discovered','at','the','video','store',',','has','apparently','sit','around','for','a','couple','of','years','without','a','distributor','.','xxmaj','it',\"'s\",'easy'...]\n"
+ ]
+ }
+ ],
+ "source": [
+ "tkn = Tokenizer(spacy)\n",
+ "print(coll_repr(tkn(txt), 31))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "There are now some tokens added that start with the characters \"xx\", which is not a common word prefix in English. These are *special tokens*.\n",
+ "\n",
+ "For example, the first item in the list, \"xxbos\", is a special token that indicates the start of a new text (\"BOS\" is a standard NLP acronym which means \"beginning of stream\"). By recognizing this start token, the model will be able to learn it needs to \"forget\" what was said previously and focus on upcoming words.\n",
+ "\n",
+ "These special tokens don't come from spaCy directly. They are there because fastai adds them by default, by applying a number of rules when processing text. These rules are designed to make it easier for a model to recognise the important parts of a sentence. In a sense, we are translating the original English language sequence into a simplified tokenised language, a language which is designed to be easy for a model to learn.\n",
+ "\n",
+ "For instance, the rules will replace a sequence of four exclamation points with a single exclamation point, followed by a special *repeated character* token, and then the number four. In this way, the model's embedding matrix can encode information about general concepts such as repeated punctuation rather than requiring a separate token for every number of repetitions of every punctuation mark. Similarly, a capitalised word will be replaced with a special capitalisation token, followed by the lower case version of the word. This way, the embedding matrix only needs the lower case version of the words, saving compute and memory, but can still learn the concept of capitalisation.\n",
+ "\n",
+ "Here are some of the main special tokens you'll see:\n",
+ "\n",
+ "- xxbos:: indicates the beginning of a text (here a review)\n",
+ "- xxmaj:: indicates the next word begins with a capital (since we lower-cased everything)\n",
+ "- xxunk:: indicates the next word is unknown\n",
+ "\n",
+ "To see the rules that were used, you can check the default rules:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "defaults.text_proc_rules"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "As always, you can look at the source code of each of them in a notebook by typing\n",
+ "\n",
+ "```\n",
+ "??replace_rep\n",
+ "```\n",
+ "\n",
+ "Here is a brief summary of what each does:\n",
+ "\n",
+ "- `fix_html`: replace special HTML characters by a readable version (IMDb reviwes have quite a few of them for instance) ;\n",
+ "- `replace_rep`: replace any character repeated three times or more by a special token for repetition (xxrep), the number of times it's repeated, then the character ;\n",
+ "- `replace_wrep`: replace any word repeated three times or more by a special token for word repetition (xxwrep), the number of times it's repeated, then the word ;\n",
+ "- `spec_add_spaces`: add spaces around / and # ;\n",
+ "- `rm_useless_spaces`: remove all repetitions of the space character ;\n",
+ "- `replace_all_caps`: lowercase a word written in all caps and adds a special token for all caps (xxcap) in front of it ;\n",
+ "- `replace_maj`: lowercase a capilaized word and adds a special token for capitalized (xxmaj) in front of it ;\n",
+ "- `lowercase`: lowercase all text and adds a special token at the beginning (xxbos) and/or the end (xxeos)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Let's take a look at a few of them in action:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"(#11) ['xxbos','©','xxmaj','fast.ai','xxrep','3','w','.fast.ai','/','xxup','index'...]\""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "coll_repr(tkn('© Fast.ai www.fast.ai/INDEX'), 31)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Subword tokenization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "In addition to the *word tokenization* approach seen in the last section, another popular tokenization method is *subword tokenization*. Word tokenization relies on an assumption that spaces provide a useful separation of components of meaning in a sentence. However, this assumption is not always appropriate. For instance, consider this sentence: 我的名字是郝杰瑞 (which means \"My name is Jeremy Howard\" in Chinese). That's not going to work very well with a word tokenizer, because there are no spaces in it! Languages like Chinese and Japanese don't use spaces, and in fact they don't even have a well-defined concept of a \"word\". There are also \"agglutinative languages\", like Polish, which can add many morphemes together to create very long \"words\" which include a lot of separate pieces of information.\n",
+ "\n",
+ "To handle these cases, it's generally best to use subword tokenization. This proceeds in two steps:\n",
+ "\n",
+ "1. Analyze a corpus of documents to find the most commonly occuring groups of letters. These become the vocab.\n",
+ "2. Tokenize the corpus using this vocab of *subword units*.\n",
+ "\n",
+ "Let's look at an example. For our corpus, we'll use the first 2000 movie reviews:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "txts = L(o.open().read() for o in files[:2000])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "We instantiate our tokenizer, passing in the size of the vocab we want to create, and then we need to \"train\" it. That is, we need to have it read our documents, and find the common sequences of characters, to create the vocab. This is done with `setup`. As we'll see shortly, `setup` is a special fastai method that is called automatically in our usual data processing pipelines. Since we're doing everything manually at the moment, however, we have to call it ourselves. Here's a function that does these steps for a given vocab size, and shows an example output:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "def subword(sz):\n",
+ " sp = SubwordTokenizer(vocab_sz=sz)\n",
+ " sp.setup(txts)\n",
+ " return ' '.join(first(sp([txt]))[:40])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Let's try it out:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'▁This ▁movie , ▁which ▁I ▁just ▁dis c over ed ▁at ▁the ▁video ▁st or e , ▁has ▁a p par ent ly ▁s it ▁around ▁for ▁a ▁couple ▁of ▁years ▁without ▁a ▁dis t ri but or . ▁It'"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "subword(1000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "When using fastai's subword tokenizer, the special character `▁` represents a space character in the original text.\n",
+ "\n",
+ "If we use a smaller vocab, then each token will represent fewer characters, and it will take more tokens to represent a sentence:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'▁ T h i s ▁movie , ▁w h i ch ▁I ▁ j us t ▁ d i s c o ver ed ▁a t ▁the ▁ v id e o ▁ st or e , ▁h a s'"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "subword(200)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "On the other hand, if we use a larger vocab, then most common English words will end up in the vocab themselves, and we will not need as many to represent a sentence:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\"▁This ▁movie , ▁which ▁I ▁just ▁discover ed ▁at ▁the ▁video ▁store , ▁has ▁apparently ▁sit ▁around ▁for ▁a ▁couple ▁of ▁years ▁without ▁a ▁distributor . ▁It ' s ▁easy ▁to ▁see ▁why . ▁The ▁story ▁of ▁two ▁friends ▁living\""
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "subword(10000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Picking a subword vocab size represents a compromise: a larger vocab means more fewer tokens per sentence, which means faster training, less memory, and less state for the model to remember; but on the downside, it means larger embedding matrices, which require more data to learn.\n",
+ "\n",
+ "Overall, subword tokenization provides a way to easily scale between character tokenization (i.e. use a small subword vocab) and word tokenization (i.e. use a large subword vocab), and handles every human language without needing language-specific algorithms to be developed. It can even handle other \"languages\" such as genomic sequences or MIDI music notation! For this reason, in the last year its popularity has soared, and it seems likely to become the most common tokenization approach (it may well already be, by the time you read this!)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Numericalization with fastai"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Numericalization is the process of mapping tokens to integers. It's basically identical to the steps necessary to create a `Category` variable, such as the dependent variable of digits in MNIST:\n",
+ "\n",
+ "1. Make a list of all possible levels of that categorical variable (the *vocab*)\n",
+ "1. Replace each level with its index in the vocab\n",
+ "\n",
+ "We'll take a look at this in action on the word-tokenized text we saw earlier:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(#228) ['xxbos','xxmaj','this','movie',',','which','i','just','discovered','at','the','video','store',',','has','apparently','sit','around','for','a','couple','of','years','without','a','distributor','.','xxmaj','it',\"'s\",'easy'...]\n"
+ ]
+ }
+ ],
+ "source": [
+ "toks = tkn(txt)\n",
+ "print(coll_repr(tkn(txt), 31))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just like `SubwordTokenizer`, we need to call `setup` on `Numericalize`; this is how we create the `vocab`. That means we'll need our tokenized corpus first. Since tokenization takes a while, it's done in parallel by fastai; but for this manual walk-thru, we'll use a small subset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#228) ['xxbos','xxmaj','this','movie',',','which','i','just','discovered','at'...]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "toks200 = txts[:200].map(tkn)\n",
+ "toks200[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can pass this to `setup` to create our `vocab`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"(#2000) ['xxunk','xxpad','xxbos','xxeos','xxfld','xxrep','xxwrep','xxup','xxmaj','the','.',',','a','and','of','to','is','in','i','it'...]\""
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "num = Numericalize()\n",
+ "num.setup(toks200)\n",
+ "coll_repr(num.vocab,20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our special rules tokens appear first, and then every word appears once, in frequency order. The defaults to `Numericalize` are `min_freq=3,max_vocab=60000`. `max_vocab=60000` results in fastai replacing all words other than the most common 60000 with a special *unknown word* token `xxunk`. This is useful to avoid having an overly large embedding matrix, since that can slow down training, use up too much memory, and can also mean that there isn't enough data to train useful representations for rare words. However, this last issue is better handled by setting `min_freq`; the default `min_freq=3` means that any word appearing less than three times is replaced with `xxunk`.\n",
+ "\n",
+ "Fastai can also numericalize your dataset using a vocab that you provide, by passing a list of words as the `vocab` parameter.\n",
+ "\n",
+ "Once we've created our `Numericalize` object, we can use it as if it's a function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 2, 8, 21, 28, 11, 90, 18, 59, 0, 45, 9, 351, 499, 11, 72, 533, 584, 146, 29, 12])"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nums = num(toks)[:20]; nums"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This time, our tokens have been converted to a tensor of integers that our model can receive. We can check that they map back to the original text:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'xxbos xxmaj this movie , which i just xxunk at the video store , has apparently sit around for a'"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "' '.join(num.vocab[o] for o in nums)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Putting our texts into batches for a language model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When dealing with images, we needed to resize them all to the same height and width before grouping them together in a mini-batch so they could stack together efficiently in a single tensor. Here it's going to be a little different, because one cannot simply resize text to a desired length. Also, we want our language model to read text in order, so that it can efficiently predict what the next word is. All the difficulty of a language model loader is that each new batch should begin precisely where the previous left off.\n",
+ "\n",
+ "Let's start with an example and imagine our text is the following:\n",
+ "\n",
+ "> : In this chapter, we will go back over the example of classifying movie reviews we studied in chapter 1 and dig deeper under the surface. First we will look at the processing steps necessary to convert text into numbers and how to customize it. By doing this, we'll have another example of the PreProcessor used in the data block API.\\nThen we will study how we build a language model and train it for a while.\n",
+ "\n",
+ "The tokenization process will add special tokens and deal with punctuation to return this text:\n",
+ "\n",
+ "> : xxbos xxmaj in this chapter , we will go back over the example of classifying movie reviews we studied in chapter 1 and dig deeper under the surface . xxmaj first we will look at the processing steps necessary to convert text into numbers and how to customize it . xxmaj by doing this , we 'll have another example of the preprocessor used in the data block xxup api . \\n xxmaj then we will study how we build a language model and train it for a while .\n",
+ "\n",
+ "We have separated the 90 tokens by spaces. Let's say we want a batch size of 6, then we need to break this text in 6 contiguous parts of length 15:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide\n",
+ "stream = \"In this chapter, we will go back over the example of classifying movie reviews we studied in chapter 1 and dig deeper under the surface. First we will look at the processing steps necessary to convert text into numbers and how to customize it. By doing this, we'll have another example of the PreProcessor used in the data block API.\\nThen we will study how we build a language model and train it for a while.\"\n",
+ "tokens = tfm(stream)\n",
+ "bs,seq_len = 6,15\n",
+ "d_tokens = np.array([tokens[i*seq_len:(i+1)*seq_len] for i in range(bs)])\n",
+ "df = pd.DataFrame(d_tokens)\n",
+ "display(HTML(df.to_html(index=False,header=None)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"TK:](\"images/att_00071.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In a perfect world, we could then give this one batch to our model. But that doesn't work, because this would very likely not fit in our GPU memory (here we have 90 tokens, but all the IMDb reviews together give several millions of tokens).\n",
+ "\n",
+ "So in fact we will need to divide this array more finely into subarrays of a fixed sequence length. It is important to maintain order within and across these subarrays, because we will use a model that maintains state in order so that it remembers what it read previously when predicting what comes next. \n",
+ "\n",
+ "Going back to our previous example with 6 batches of length 15, if we chose sequence length of 5, that would mean we first feed the following array:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "bs,seq_len = 6,5\n",
+ "d_tokens = np.array([tokens[i*15:i*15+seq_len] for i in range(bs)])\n",
+ "df = pd.DataFrame(d_tokens)\n",
+ "display(HTML(df.to_html(index=False,header=None)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "bs,seq_len = 6,5\n",
+ "d_tokens = np.array([tokens[i*15+seq_len:i*15+2*seq_len] for i in range(bs)])\n",
+ "df = pd.DataFrame(d_tokens)\n",
+ "display(HTML(df.to_html(index=False,header=None)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And finally"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#hide_input\n",
+ "bs,seq_len = 6,5\n",
+ "d_tokens = np.array([tokens[i*15+10:i*15+15] for i in range(bs)])\n",
+ "df = pd.DataFrame(d_tokens)\n",
+ "display(HTML(df.to_html(index=False,header=None)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Going back to our dataset, the first step is to transform the individual texts into a stream by concatenating them together. As with images, it's best to randomize the order in which the inputs come, so at the beginnaing of each epoch we will shuffle the entries to make a new stream (we shuffle the order of the documents, not the order of the words inside, otherwise the text would not make sense anymore).\n",
+ "\n",
+ "We will then cut this stream into a certain number of batches (which is our *batch size*). For instance, if the stream has 50,000 tokens and we set a batch size of 10, this will give us 10 mini-streams of 5,000 tokens. What is important is that we preserve the order of the tokens (so from 1 to 5,000 for the first mini-stream, then from 5,001 to 10,000...) because we want the model to read continuous rows of text (as in our example above). This is why each text has been added a `xxbos` token during preprocessing, so that the model knows when it reads the stream we are beginning a new entry.\n",
+ "\n",
+ "So to recap, at every epoch we shuffle our collection of documents to pick one docment, and then we transform that one into a stream of tokens. We then cut that stream into a batch of fixed-size consecutive mini-streams. Our model will then read the mini-streams in order, and thanks to an inner state, it will produce the same activation whatever sequence length you picked.\n",
+ "\n",
+ "This is all done behind the scenes by the fastai library when we create a `LMDataLoader`. We can create one by first applying our `Numericalize` object to the tokenized texts:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nums200 = toks200.map(num)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and then passing that to `LMDataLoader`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl = LMDataLoader(nums200)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's confirm that this gives the expected results, by grabbing the first batch:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([64, 72]), torch.Size([64, 72]))"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x,y = first(dl)\n",
+ "x.shape,y.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and then looking at the first row of the independent variable, which should be the start of the first text:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'xxbos xxmaj this movie , which i just xxunk at the video store , has apparently sit around for a'"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "' '.join(num.vocab[o] for o in x[0][:20])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and the first row of the dependent variable, which is the same thing offset by one token:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'xxmaj this movie , which i just xxunk at the video store , has apparently sit around for a couple'"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "' '.join(num.vocab[o] for o in y[0][:20])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Training a text classifier"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Language model using DataBlock"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "fastai handles tokenization and numericalization automatically when `TextBlock` is passed to `DataBlock`. All of the arguments that can be passed to `Tokenize` and `Numericalize` can also be passed to `TextBlock`. In the next chapter we'll discuss the easiest ways to run each of these steps separately, to ease debugging--but you can always just debug by running them manually on a subset of your data as shown in the previous sections. And don't forget about `DataBlock`'s handy `summary` method, which is very useful for debugging data issues.\n",
+ "\n",
+ "Here's how we use `TextBlock` to create a language model, using fastai's defaults:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "get_imdb = partial(get_text_files, folders=['train', 'test', 'unsup'])\n",
+ "\n",
+ "dls_lm = DataBlock(\n",
+ " blocks=TextBlock.from_folder(path, is_lm=True),\n",
+ " get_items=get_imdb, splitter=RandomSplitter(0.1)\n",
+ ").dataloaders(path, path=path, bs=128, seq_len=80)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "One thing that's different to previous types used in `DataBlock` is that we're not just using the class directly (i.e. `TextBlock(...)`, but instead are calling a *class method*. A class method is a Python method which, as the name suggests, belongs to a *class* rather than an *object*. (Be sure to search online for more information about class methods if you're not familiar with them, since they're commonly used in many Python libraries and applications; we've used them a few times previously in the book, but haven't called attention to them.) The reason that `TextBlock` is special is that setting up the numericalizer's vocab can take a long time (we have to read every document and tokenize it to get the vocab); to be as efficient as possible fastai does things such as: \n",
+ "\n",
+ "- Save the tokenized documents in a temporary folder, so fastai doesn't have to tokenize more than once\n",
+ "- Runs multiple tokenization processes in parallel, to take advantage of your computer's CPUs.\n",
+ "\n",
+ "Therefore we need to tell `TextBlock` how to access the texts, so that it can do this initial preprocessing--that's what `from_folder` does.\n",
+ "\n",
+ "`show_batch` then works in the usual way:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls_lm.show_batch(max_n=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Fine tuning the language model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "For converting the integer word indices into activations that we can use for our neural network, we will use embeddings, just like we did for collaborative filtering and tabular modelling. Then those embeddings are fed in a *Recurrent Neural Network* (RNN), using an architecture called *AWD_LSTM* (we will show how to write such a model from scratch in <>). As we discussed earlier, the embeddings in the pretrained model are merged with random embeddings added for words that weren't in the pretraining vocabulary. This is handled automatically inside `language_model_learner`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [],
+ "source": [
+ "learn = language_model_learner(\n",
+ " dls_lm, AWD_LSTM, drop_mult=0.3, \n",
+ " metrics=[accuracy, Perplexity()]).to_fp16()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "The loss function used by default is cross entropy loss, since we essentially have a classification problem (the different categories being the words in our vocab). A metric often used in NLP for language models is called *perplexity*. It is the exponential of the loss (i.e. `torch.exp(cross_entropy)`). We will also add accuracy, to see how many times our model is right when trying to predict the next word, since cross entropy (as we've seen) is both hard to interpret, and also tells you more about the model's confidence, rather than just its accuracy\n",
+ "\n",
+ "The grey first arrow in our overall picture has been done for us and made available as a pretrained model in fastai; we've now built the `DataLoaders` and `Learner` for the second stage, and we're ready to fine-tune it!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "It takes quite a while to train each epoch, so we'll be saving the intermediate model results during the training process. Since `fine_tune` doesn't do that for us, we'll just use `fit_one_cycle`. Just like `cnn_learner`, `language_model_learner` automatically calls `freeze` when using a pretrained model (which is the default), so this will only train the embeddings (which is the only part of the model that contains randomly initialized weights--i.e. embeddings for words that are in our IMDb vocab, but aren't in the pretrained model vocab):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(1, 2e-2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Saving and loading models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model takes a while to train, so it's a good opportunity to talk about saving intermediary results. You can easily save the state of your model like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.save('1epoch')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It will create a file in `learn.path/models/` named \"1epoch.pth\". If you want to load your model in another machine after creating your `Learner` the same way, or resume training later, you can load the content of this file with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = learn.load('1epoch')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can them finetune the model after unfreezing:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.unfreeze()\n",
+ "learn.fit_one_cycle(10, 2e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Once this is done, we save all of our model except the final layer that converts activations to probabilities of picking each token in our vocabulary. The model not without the final layer is called the *encoder*. We can save it with `save_encoder`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.save_encoder('finetuned')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Encoder: The model not including the task-specific final layer(s). It means much the same thing as *body* when applied to vision CNNs, but tends to be more used for NLP and generative models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This completes the second stage of the text classification process: fine-tuning the language model. We can now fine tune this language model using the IMDb sentiment labels."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Text generation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "Before using this to fine-tune a classifier on the review, we can use our model to generate random reviews: since it's trained to guess what the next word of the sentence is, we can use it to write new reviews:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "TEXT = \"I liked this movie because\"\n",
+ "N_WORDS = 40\n",
+ "N_SENTENCES = 2\n",
+ "preds = [learn.predict(TEXT, N_WORDS, temperature=0.75) for _ in range(N_SENTENCES)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i liked this movie because of its story and characters . The story line was very strong , very good for a sci - fi film . The main character , Alucard , was very well developed and brought the whole story\n",
+ "i liked this movie because i like the idea of the premise of the movie , the ( very ) convenient virus ( which , when you have to kill a few people , the \" evil \" machine has to be used to protect\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"\\n\".join(preds))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "As you can see, we add some randomness (we pick a random word based on the probabilities returned by the model) so you don't get exactly the same review twice. Our model doesn't have any programmed knowledge of the structure of a sentence or grammar rules, yet it has clearly learned a lot about English sentences: we can see it capitalized properly (I is just transformed to i with our rules -- they require two characters or more to consider a word is capitalized -- so it's normal to see it lowercased), and is using consistent tense. The general review make sense at first glance, and it's only if you read carefully you can notice something is a bit off. Not bad for a model trained in a couple of hours! \n",
+ "\n",
+ "Our end goal wasn't to train a model to generate reviews, but to classify them... so let's use this model to do just that."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating the classifier DataLoaders"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're now moving from language model fine tuning, to classifier fine tuning. To re-cap, a language model predicts the next word of a document, so it doesn't need any external labels. A classifier, however, predicts some external label--in the case of IMDb, it's the sentiment of a document.\n",
+ "\n",
+ "This means that the structure of our `DataBlock` for NLP classification will look very familiar; it's actually nearly the same as we've seen for the many image classification datasets we've worked with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dls_clas = DataBlock(\n",
+ " blocks=(TextBlock.from_folder(path, vocab=dls_lm.vocab),CategoryBlock),\n",
+ " get_y = parent_label,\n",
+ " get_items=partial(get_text_files, folders=['train', 'test']),\n",
+ " splitter=GrandparentSplitter(valid_name='test')\n",
+ ").dataloaders(path, path=path, bs=128, seq_len=72)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just like with image classification, `show_batch` shows the dependent variable (sentiment, in this case) with each independent variable (movie review text):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls_clas.show_batch(max_n=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the `DataBlock` definition above, every piece is familiar from previous data blocks we've built, with two important exceptions:\n",
+ "\n",
+ "- `TextBlock.from_folder` no longer has the `is_lm=True` parameter, and\n",
+ "- We pass the `vocab` we created for the language model fine-tuning.\n",
+ "\n",
+ "The reason that we pass the vocab of the language model is to make sure we use the same correspondence of token to index. Otherwise the embeddings we learned in our fine-tuned language model won't make any sense to this model, and the fine-tuning step won't be of any use.\n",
+ "\n",
+ "By passing `is_lm=False` (or not passing `is_lm` at all, since it defaults to `False`) we tell `TextBlock` that we have regular labeled data, rather than using the next tokens as labels. There is one challenge we have to deal with, however, which is to do with collating multiple documents into a minibatch. Let's see with an example, by trying to create a minibatch containing the first 10 documents. First we'll numericalize them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nums_samp = toks200[:10].map(num)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's now look at how many tokens each of these 10 movie reviews have:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#10) [228,238,121,290,196,194,533,124,581,155]"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nums_samp.map(len)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Remember, PyTorch `DataLoader`s need to collate all the items in a batch into a single tensor, and that a single tensor has a fixed shape (i.e. it has some particular length on every axis, and all items must be consistent). This should look a bit familiar: we had the same issue with images. In that case, we use cropping, padding, and/or squishing to make everything the same size. Cropping might not be a good idea for documents, because it seems likely we'd remove some key information (having said that, the same issue is true for images, and we use cropping there; data augmentation hasn't been well explored for NLP yet, so perhaps there are actually opportunities to use cropping in NLP too!) You can't really \"squish\" a document. So that leaves padding!\n",
+ "\n",
+ "We will expand the shortest texts to make them all the same size. To do this, we use a special token that will be ignored by our model. This is called *padding* (just like in vision). Additionally, to avoid memory issues and improve performance, we will batch together texts that are roughly the same lengths (with some shuffling for the training set). We do this by (approximately, for the training set) sorting the documents by length prior to each epoch. The result of this is that the documents collated into a single batch will tend of be of similar lengths. We won't make every batch, therefore, the same size, but will instead use the size of the largest document in each batch. (It is possible to do something similar with images, which is especially useful for irregularly sized rectangular images, although as we write these words, no library provides good support for this yet, and there aren't any papers covering it. It's something we're planning to add to fastai soon however, so have a look on the book website, where we'll add information about this if and when it's working well.)\n",
+ "\n",
+ "The padding and sorting is automatically done by the data block API for us when using a `TextBlock`, with `is_lm=False`. (We don't have this same issue for language model data, since we concatenate all the documents together first, and then split them into equally sized sections.)\n",
+ "\n",
+ "We can now create a model to classify our texts:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = text_classifier_learner(dls_clas, AWD_LSTM, drop_mult=0.5, metrics=accuracy).to_fp16()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The final step prior to training the classifier is to load the encoder from our fine-tuned language model. We use `load_encoder` instead of `load` because we only have pretrained weights available for the encoder; `load` by default raises an exception if an incomplete model is loaded."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = learn.load_encoder('finetuned')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Fine tuning the classifier"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The last step is to train with discriminative learning rates and *gradual unfreezing*. In computer vision, we often unfreeze the model all at once, but for NLP classifiers, we find that unfreezing a few layers at a time makes a real difference."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(1, 2e-2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In just one epoch we get the same result as our training in <>, not too bad! We can pass `-2` to `freeze_to` to freeze all except the last two parameter groups:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.freeze_to(-2)\n",
+ "learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we can unfreeze a bit more, and continue training:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.freeze_to(-3)\n",
+ "learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And finally, the whole model!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.unfreeze()\n",
+ "learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We reach 94.3% accuracy, which was state-of-the-art just three years ago. By training a model on all the texts read backwards and averaging the predictions of those two models, we can even get to 95.1% accuracy, which was the state of the art introduced by the ULMFiT paper. It was only beaten a few months ago, fine-tuning a much bigger model and using expensive data augmentation (translating sentences in another language and back, using another model for translation)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Disinformation and language models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Even simple algorithms based on rules, before the days of widely available deep learning language models, could be used to create fraudulent accounts and try to influence policymakers. Jeff Kao, now a computational journalist at ProPublica, analysed the comments that were sent to the FCC in the USA regarding a 2017 proposal to repeal net neutrality. In his article [More than a Million Pro-Repeal Net Neutrality Comments were Likely Faked](https://hackernoon.com/more-than-a-million-pro-repeal-net-neutrality-comments-were-likely-faked-e9f0e3ed36a6)\", he discovered a large cluster of comments opposing net neutrality that seemed to have been generated by some sort of Madlibs-style mail merge. Below, the fake comments have been helpfully color-coded by Kao to highlight their formulaic nature:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"images/ethics/image16.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Kao estimated that \"less than 800,000 of the 22M+ comments… could be considered truly unique\" and that \"more than 99% of the truly unique comments were in favor of keeping net neutrality.\"\n",
+ "\n",
+ "Given advances in language modeling that have occurred since 2017, such fraudulent campaigns could be nearly impossible to catch now. You now have all the tools at your disposal necessary to create and compelling language model. That is, something that can generate context appropriate believable text. It won't necessarily be perfectly accurate or correct, but it will be believable. Think about what this technology would mean when put together with the kinds of disinformation campaigns we have learned about. Take a look at this conversation on Reddit, where a language model based on OpenAI's GPT-2 algorithm is having a conversation with itself about whether the US government should cut defense spending:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"An](\"images/ethics/image14.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, the use of the algorithm is being done explicitly. But imagine what would happen if a bad actor decided to release such an algorithm across social networks. They could do it slowly and carefully, allowing the algorithms to gradually develop followings and trust over time. It would not take many resources to have literally millions of accounts doing this. In such a situation we could easily imagine it getting to a point where the vast majority of discourse online was from bots, and nobody would have any idea that it was happening.\n",
+ "\n",
+ "We are already starting to see examples of machine learning being used to generate identities. For example, here is the LinkedIn profile for Katie Jones:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"images/ethics/image15.jpeg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Katie Jones was connected on LinkedIn to several members of mainstream Washington think tanks. But she didn't exist. That image you see is auto generated by a generative adversarial network, and somebody named Katie Jones has not, in fact, graduated from the Centre for Strategic and International Studies.\n",
+ "\n",
+ "Many people assume or hope that algorithms will come to our defence here. The hope is that we will develop classification algorithms which can automatically recognise auto generated content. The problem, however, is that this will always be an arms race, in which better classification (or discriminator) algorithms can be used to create better generation algorithms."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What is self-supervised learning?\n",
+ "1. What is a language model?\n",
+ "1. Why is a language model considered self-supervised learning?\n",
+ "1. What are self-supervised models usually used for?\n",
+ "1. What do we fine-tune language models?\n",
+ "1. What are the three steps to create a state-of-the-art text classifier?\n",
+ "1. How do the 50,000 unlabeled movie reviews help create a better text classifier for the IMDb dataset?\n",
+ "1. What are the three steps to prepare your data for a language model?\n",
+ "1. What is tokenization? Why do we need it?\n",
+ "1. Name three different approaches to tokenization.\n",
+ "1. What is 'xxbos'?\n",
+ "1. List 4 rules that fastai applies to text during tokenization.\n",
+ "1. Why are repeated characters replaced with a token showing the number of repetitions, and the character that's repeated?\n",
+ "1. What is numericalization?\n",
+ "1. Why might there be words that are replaced with the \"unknown word\" token?\n",
+ "1. With a batch size of 64, the first row of the tensor representing the first batch contains the first 64 tokens for the dataset. What does the second row of that tensor contain? What does the first row of the second batch contain? (Careful—students often get this one wrong! Be sure to check your answer against the book website.)\n",
+ "1. Why do we need padding for text classification? Why don't we need it for language modeling?\n",
+ "1. What does an embedding matrix for NLP contain? What is its shape?\n",
+ "1. What is perplexity?\n",
+ "1. Why do we have to pass the vocabulary of the language model to the classifier data block?\n",
+ "1. What is gradual unfreezing?\n",
+ "1. Why is text generation always likely to be ahead of automatic identification of machine generated texts?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. See what you can learn about language models and disinformation. What are the best language models today? Have a look at some of their outputs. Do you find them convincing? How could a bad actor best use this to create conflict and uncertainty?\n",
+ "1. Given the limitation that models are unlikely to be able to consistently recognise machine generated texts, what other approaches may be needed to handle large-scale disinformation campaigns that leveraged deep learning?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Becoming a deep learning practitioner"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Congratulations — you've completed all of the chapters in this book which cover the key practical parts of training and using deep learning! You know how to use all of fastai's built in applications, and how to customise them using the data blocks API and loss functions. You even know how to create a neural network from scratch, and train it! (And hopefully you now know some of the questions to ask to help make sure your creations help improve society too.)\n",
+ "\n",
+ "The knowledge you already have is enough to create full working prototypes of many types of neural network application. More importantly, it will help you understand the capabilities and limitations of deep learning models, and how to design a system which best handles these capabilities and limitations.\n",
+ "\n",
+ "In the rest of this book we will be pulling apart these applications, piece by piece, to understand all of the foundations they are built on. This is important knowledge for a deep learning practitioner, because it is the knowledge which allows you to inspect and debug models that you build, and to create new applications which are customised for your particular projects."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {
+ "height": "367.997px",
+ "width": "278.999px"
+ },
+ "number_sections": false,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/11_nlp_dive.ipynb b/11_nlp_dive.ipynb
new file mode 100644
index 000000000..73a197643
--- /dev/null
+++ b/11_nlp_dive.ipynb
@@ -0,0 +1,1313 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_nlp_dive]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A language model from scratch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're now ready to go deep... deep into deep learning! You already learned how to train a basic neural network, but how do you go from there to creating state of the art models? In this part of the book we're going to uncover all of the mysteries, starting with language models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Whenever we start working on a new problem, we always first try to think of the simplest dataset we can which would allow us to try out methods quickly and easily, and interpret the results. When we started working on language modelling a few years ago, we didn't find any datasets that would allow for quick prototyping, so we made one. We call it *human numbers*, and it simply contains the first 10,000 words written out in English."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: One of the most common practical mistakes I see even amongst highly experienced practitioners is failing to use appropriate datasets at appropriate times during the analysis process. In particular, most people tend to start with datasets which are too big and too complicated."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can download, extract, and take a look at our dataset in the usual way:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.text.all import *\n",
+ "path = untar_data(URLs.HUMAN_NUMBERS)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#2) [Path('train.txt'),Path('valid.txt')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's open those two files and see what's inside. At first we'll join all of those texts together and ignore the split train/valid given by the dataset, we will come back to it later on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#9998) ['one \\n','two \\n','three \\n','four \\n','five \\n','six \\n','seven \\n','eight \\n','nine \\n','ten \\n'...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lines = L()\n",
+ "with open(path/'train.txt') as f: lines += L(*f.readlines())\n",
+ "with open(path/'valid.txt') as f: lines += L(*f.readlines())\n",
+ "lines"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We take all those lines and concatenate them in one big stream. To mark when we go from one number to the next, we use a '.' as separation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'one . two . three . four . five . six . seven . eight . nine . ten . eleven . twelve . thirteen . fo'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text = ' . '.join([l.strip() for l in lines])\n",
+ "text[:100]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's use word tokenization for this dataset, by splitting on spaces:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['one', '.', 'two', '.', 'three', '.', 'four', '.', 'five', '.']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokens = text.split(' ')\n",
+ "tokens[:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To numericalize, we have to create a list of all the unique tokens (our *vocab*):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#30) ['one','.','two','three','four','five','six','seven','eight','nine'...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab = L(*tokens).unique()\n",
+ "vocab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we can convert our tokens into numbers by looking up the index of each in the vocab:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#63095) [0,1,2,1,3,1,4,1,5,1...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "word2idx = {w:i for i,w in enumerate(vocab)}\n",
+ "nums = L(word2idx[i] for i in tokens)\n",
+ "nums"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Our first language model from scratch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One simple way to turn this into a neural network would be to specify that we are going to predict each word based on the previous three words. Therefore, we could create a list of every sequence of three words as independent variables, and the next word after each sequence as the dependent variable. \n",
+ "\n",
+ "We can do that with plain Python. Let us do it first with tokens just to confirm what it looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#21031) [(['one', '.', 'two'], '.'),(['.', 'three', '.'], 'four'),(['four', '.', 'five'], '.'),(['.', 'six', '.'], 'seven'),(['seven', '.', 'eight'], '.'),(['.', 'nine', '.'], 'ten'),(['ten', '.', 'eleven'], '.'),(['.', 'twelve', '.'], 'thirteen'),(['thirteen', '.', 'fourteen'], '.'),(['.', 'fifteen', '.'], 'sixteen')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L((tokens[i:i+3], tokens[i+3]) for i in range(0,len(tokens)-4,3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we will do it with tensors of the numericalized values, which is what the model will actually use:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#21031) [(tensor([0, 1, 2]), 1),(tensor([1, 3, 1]), 4),(tensor([4, 1, 5]), 1),(tensor([1, 6, 1]), 7),(tensor([7, 1, 8]), 1),(tensor([1, 9, 1]), 10),(tensor([10, 1, 11]), 1),(tensor([ 1, 12, 1]), 13),(tensor([13, 1, 14]), 1),(tensor([ 1, 15, 1]), 16)...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "seqs = L((tensor(nums[i:i+3]), nums[i+3]) for i in range(0,len(nums)-4,3))\n",
+ "seqs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we can batch those easily using the `DataLoader` class. For now we will split randomly the sequences."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bs = 64\n",
+ "cut = int(len(seqs) * 0.8)\n",
+ "dls = DataLoaders.from_dsets(seqs[:cut], seqs[cut:], bs=64, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create a neural network architecture that takes three words as input, and returns a prediction of the probability of each possible next word in the vocab. We will use three standard linear layers, but with two tweaks.\n",
+ "\n",
+ "The first tweak is that the first linear layer will use only the first word's embedding as activations, the second layer will use the second word's embedding plus the first layer's output activations, and the third layer will use the third word's embedding plus the second layer's output activations. The key effect of this is that every word is interpreted in the information context of any words preceding it. \n",
+ "\n",
+ "The second tweak is that each of these three layers will use the same weight matrix. The way that one word impacts the activations from previous words should not change depending on the position of a word. In other words, activation values will change as data moves through the layers, but the layer weights themselves will not change from layer to layer. So a layer does not learn one sequence position; it must learn to handle all positions.\n",
+ "\n",
+ "Since layer weights do not change, you might think of the sequential layers as the \"same layer\" repeated. In fact PyTorch makes this concrete; we can just create one layer, and use it multiple times."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Our language model in PyTorch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create the language model module that we described earlier:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel1(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden) \n",
+ " self.h_h = nn.Linear(n_hidden, n_hidden) \n",
+ " self.h_o = nn.Linear(n_hidden,vocab_sz)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " h = F.relu(self.h_h(self.i_h(x[:,0])))\n",
+ " h = h + self.i_h(x[:,1])\n",
+ " h = F.relu(self.h_h(h))\n",
+ " h = h + self.i_h(x[:,2])\n",
+ " h = F.relu(self.h_h(h))\n",
+ " return self.h_o(h)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you see, we have created three layers:\n",
+ "\n",
+ "- The embedding layer (`i_h` for *input* to *hidden*)\n",
+ "- The linear layer to create the activations for the next word (`h_h` for *hidden* to *hidden*)\n",
+ "- A final linear layer to predict the fourth word (`h_o` for *hidden* to *output*)\n",
+ "\n",
+ "This might be easier to represent in pictorial form. Let's define a simple pictorial representation of basic neural networks. Here's how we're going to represent a neural net with one hidden layer:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Pictorial](\"images/att_00020.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each shape represents activations: rectangle for input, circle for hidden (inner) layer activations, and triangle for output activations:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Shapes](\"images/att_00021.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "An arrow represents the actual layer computation—i.e. the linear layer followed by the activation layers. Using this notation, here's what our simple language model looks like:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Representation](\"images/att_00022.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To simplify things, we've removed the details of the layer computation from each arrow. We've also color-coded the arrows, such that all arrows with the same color have the same weight matrix. For instance, all the input layers use the same embedding matrix, so they all have the same color (green).\n",
+ "\n",
+ "Let's try training this model and see how it goes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = Learner(dls, LMModel1(len(vocab), 64), loss_func=F.cross_entropy, metrics=accuracy)\n",
+ "learn.fit_one_cycle(4, 1e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see if this is any good, let's check what would a very simple model give us. In this case we could always predict the most common token, so let's find out which token is the most often the target in our validation set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor(29), 'thousand', 0.15165200855716662)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n,counts = 0,torch.zeros(len(vocab))\n",
+ "for x,y in dls.valid:\n",
+ " n += y.shape[0]\n",
+ " for i in range_of(vocab): counts[i] += (y==i).long().sum()\n",
+ "idx = torch.argmax(counts)\n",
+ "idx, vocab[idx.item()], counts[idx].item()/n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The most common token has the index 29, which corresponds to the token 'thousand'. Always predicting this token would give us an accuracy of roughly 15\\%, so we are faring way better!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> A: My first guess was that the separator would be the most common token, since there is one for every number. But looking at `tokens` reminded me that large numbers are written with many words, so on the way to 10,000 you write \"thousand\" a lot: five thousand, five thousand and one, five thousand and two, etc.. Oops! Looking at your data is great for noticing subtle featues and also embarrassingly obvious ones."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Our first recurrent neural network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the code for our module, we could simplify it by replacing the duplicated code that calls the layers with a for loop. As well as making our code simpler, this will also have the benefit that we could apply our module equally well to token sequences of different lengths; we would not be restricted to token lists of length three."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel2(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden) \n",
+ " self.h_h = nn.Linear(n_hidden, n_hidden) \n",
+ " self.h_o = nn.Linear(n_hidden,vocab_sz)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " h = 0\n",
+ " for i in range(3):\n",
+ " h = h + self.i_h(x[:,i])\n",
+ " h = F.relu(self.h_h(h))\n",
+ " return self.h_o(h)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's check that we get the same results using this refactoring:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = Learner(dls, LMModel2(len(vocab), 64), loss_func=F.cross_entropy, metrics=accuracy)\n",
+ "learn.fit_one_cycle(4, 1e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also refactor our pictorial representation in exactly the same way (we're also removing the details of activation sizes here, and using the same arrow colors as the previous diagram):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Basic](\"images/att_00070.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You will see that there is a set of activations which are being updated each time through the loop, and are stored in the variable `h` — this is called the *hidden state*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> Jargon: hidden state: the activations that are updated at each step of a recurrent neural network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A neural network which is defined using a loop like this is called a *recurrent neural network*, also known as an RNN. It is important to realise that an RNN is not a complicated new architecture, but is simply a refactoring of a multilayer neural network using a for loop.\n",
+ "\n",
+ "> A: My true opinion: if they were called \"looping neural networks\", or LNNs, they would seem 50% less daunting!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Improving the RNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Maintaining the state of an RNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the code for our RNN, one thing that seems problematic is that we are initialising our hidden state to zero for every new input sequence. Why is that a problem? We made our sample sequences short so they would fit easily into batches. But if we order those samples correctly, those sample sequences will be read in order by the model, exposing the model to long stretches of the original sequence. \n",
+ "\n",
+ "But because we initialize the model's hidden state to zero for each new sample, we are throwing away all the information we have about the sentences we have seen so far, which means that our model doesn't actually know where we are up to in the overall counting sequence. This is easily fixed; we can simply move the initialisation of the hidden state to `__init__`.\n",
+ "\n",
+ "But this fix will create its own subtle, but important, problem. It effectively makes our neural network as deep as the entire number of tokens in our document. For instance, if there were 10,000 tokens in our dataset, we would be creating a 10,000 layer neural network.\n",
+ "\n",
+ "To see this, consider the original pictorial representation of our recurrent neural network, before refactoring it with a for loop. You can see each layer corresponds with one token input. When we talk about the representation of a recurrent neural network before refactoring with the for loop, we call this the *unrolled representation*. It is often helpful to consider the unrolled representation when trying to understand an RNN.\n",
+ "\n",
+ "The problem with a 10,000 layer neural network is that if and when you get to the 10,000th word of the dataset, you will still need to calculate the derivatives all the way back to the first layer. This is going to be very slow indeed, and very memory intensive. It is unlikely that you could store even one mini batch on your GPU.\n",
+ "\n",
+ "The solution to this is to tell PyTorch that we do not want to back propagate the derivatives through the entire implicit neural network. Instead, we will just keep the last three layers of gradients. To remove all of the gradient history in PyTorch, we use the `detach` method.\n",
+ "\n",
+ "Here is the new version of our RNN. It is now stateful, because it remembers its activations between different calls to `forward`, which represent its use for different samples in the batch:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel3(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden) \n",
+ " self.h_h = nn.Linear(n_hidden, n_hidden) \n",
+ " self.h_o = nn.Linear(n_hidden,vocab_sz)\n",
+ " self.h = 0\n",
+ " \n",
+ " def forward(self, x):\n",
+ " for i in range(3):\n",
+ " self.h = self.h + self.i_h(x[:,i])\n",
+ " self.h = F.relu(self.h_h(self.h))\n",
+ " out = self.h_o(self.h)\n",
+ " self.h = self.h.detach()\n",
+ " return out\n",
+ " \n",
+ " def reset(self): self.h = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you think about it, this model will have the same activations whatever the sequence length we pick, because the hidden state will remember the last activation from the previous batch. The only thing that will be different are the gradients computed at each step: they will only be calculated on sequence length tokens in the past, instead of the whole stream. That is why this sequence length is often called *bptt* for back-propagation through time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* jargon: Back propagation through time (BPTT): Treating a neural net with effectively one layer per time step (usually refactored using a loop) as one big model, and calculating gradients on it in the usual way. To avoid running out of memory and time, we usually use _truncated_ BPTT, which \"detaches\" the history of computation steps in the hidden state every few time steps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To use `LMModel3`, we need to make sure the samples are going to be seen in a certain order. As we saw in the previous chapter, if the first line of the first batch is our `dset[0]` then the second batch should have `dset[1]` as the first line, so that the model sees the text flowing.\n",
+ "\n",
+ "`LMDataLoader` was doing this for us in the previous chapter. This time we're going to do it ourselves.\n",
+ "\n",
+ "To do this, we are going to rearrange our dataset. First we divide the samples into `m = len(dset) // bs` groups (this is the equivalent of splitting the whole concatenated dataset into, for instance, 64 equally sized pieces, since we're using `bs=64` here). `m` is the length of each of these pieces. For instance, if we're using our whole dataset (although we'll actually split it into train vs valid in a moment), that will be:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(328, 64, 21031)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = len(seqs)//bs\n",
+ "m,bs,len(seqs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first batch will be composed of the samples:\n",
+ "\n",
+ " (0, m, 2*m, ..., (bs-1)*m)\n",
+ "\n",
+ "then the second batch of the samples: \n",
+ "\n",
+ " (1, m+1, 2*m+1, ..., (bs-1)*m+1)\n",
+ "\n",
+ "and so forth. This way, at each epoch, the model will see a chunk of contiguous text of size `3*m` (since each text is of size 3) on each line of the batch.\n",
+ "\n",
+ "The following function does that reindexing:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def group_chunks(ds, bs):\n",
+ " m = len(ds) // bs\n",
+ " new_ds = L()\n",
+ " for i in range(m): new_ds += L(ds[i + m*j] for j in range(bs))\n",
+ " return new_ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we just pass `drop_last=True` when building our `DataLoaders` to drop the last batch that has not a shape of `bs`, we also pass `shuffle=False` to make sure the texts are read in order."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cut = int(len(seqs) * 0.8)\n",
+ "dls = DataLoaders.from_dsets(group_chunks(seqs[:cut], bs), group_chunks(seqs[cut:], bs), bs=bs, drop_last=True, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The last thing we add is a little tweak of the training loop via a `Callback`. We will talk more about callbacks in <>; this one will call the `reset` method of our model at the beginning of each epoch and before each validation phase. Since we implemented that method to zero the hidden state of the model, this will make sure we start we a clean state before reading those continuous chunks of text. We can also start training a bit longer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = Learner(dls, LMModel3(len(vocab), 64), loss_func=F.cross_entropy,\n",
+ " metrics=accuracy, cbs=ModelReseter)\n",
+ "learn.fit_one_cycle(10, 3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating more signal"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another problem with our current approach is that we only predict one output word for each three input words. That means that the amount of signal that we are feeding back to update weights with is not as large as it could be. It would be better if we predicted the next word after every single word, rather than every three words. Here's the pictorial version:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"RNN](\"images/att_00024.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is easy enough to add. We need to first change our data so that the dependent variable has each of the three next words after each of our three input words. Instead of 3, we use an attribute, `sl` (for sequence length) and make it a bit bigger:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sl = 16\n",
+ "seqs = L((tensor(nums[i:i+sl]), tensor(nums[i+1:i+sl+1]))\n",
+ " for i in range(0,len(nums)-sl-1,sl))\n",
+ "cut = int(len(seqs) * 0.8)\n",
+ "dls = DataLoaders.from_dsets(group_chunks(seqs[:cut], bs),\n",
+ " group_chunks(seqs[cut:], bs),\n",
+ " bs=bs, drop_last=True, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the first element of `seqs`, we can see that it contains two lists of the same size. The second list is the same as the first, but offset by one element:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(#16) ['one','.','two','.','three','.','four','.','five','.'...],\n",
+ " (#16) ['.','two','.','three','.','four','.','five','.','six'...]]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[L(vocab[o] for o in s) for s in seqs[0]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need to modify our model so that it outputs a prediction after every word, rather than just at the end of a three word sequence:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel4(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden) \n",
+ " self.h_h = nn.Linear(n_hidden, n_hidden) \n",
+ " self.h_o = nn.Linear(n_hidden,vocab_sz)\n",
+ " self.h = 0\n",
+ " \n",
+ " def forward(self, x):\n",
+ " outs = []\n",
+ " for i in range(sl):\n",
+ " self.h = self.h + self.i_h(x[:,i])\n",
+ " self.h = F.relu(self.h_h(self.h))\n",
+ " outs.append(self.h_o(self.h))\n",
+ " self.h = self.h.detach()\n",
+ " return torch.stack(outs, dim=1)\n",
+ " \n",
+ " def reset(self): self.h = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model will return outputs of shape `bs x sl x vocab_sz` (since we stacked on `dim=1`). Our targets are of shape `bs x sl`, so we need to flatten those before using them in `F.cross_entropy`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def loss_func(inp, targ):\n",
+ " return F.cross_entropy(inp.view(-1, len(vocab)), targ.view(-1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now use this loss function to train the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = Learner(dls, LMModel4(len(vocab), 64), loss_func=loss_func,\n",
+ " metrics=accuracy, cbs=ModelReseter)\n",
+ "learn.fit_one_cycle(15, 3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need to train for longer, since the task has changed a bit and is more complicated now. But we end up with a good result... At least, sometimes. If you run it a few times, you'll see that you can get quite different results on different runs. That's because effectively we have a very deep network here, which can result in very large or very small gradients. We'll see in the next chapter how to resolve this, by using the `LSTM` architecture.\n",
+ "\n",
+ "We can also see that `valid_loss` is getting worse, so it may help to add some additional regularization. That will be provided by the `AWD` variant of `LSTM`, which we'll also see in the next chapter.\n",
+ "\n",
+ "By combining these techniques, we'll see how to get around 85% accuracy on this dataset!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. If the dataset for your project is so big and complicated that working with it takes a significant amount of time, what should you do?\n",
+ "1. Why do we concatenating the documents in our dataset before creating a language model?\n",
+ "1. To use a standard fully connected network to predict the fourth word given the previous three words, what two tweaks do we need to make?\n",
+ "1. How can we share a weight matrix across multiple layers in PyTorch?\n",
+ "1. Write a module which predicts the third word given the previous two words of a sentence, without peeking.\n",
+ "1. What is a recurrent neural network?\n",
+ "1. What is hidden state?\n",
+ "1. What is the equivalent of hidden state in ` LMModel1`?\n",
+ "1. To maintain the state in an RNN why is it important to pass the text to the model in order?\n",
+ "1. What is an unrolled representation of an RNN?\n",
+ "1. Why can maintaining the hidden state in an RNN lead to memory and performance problems? How do we fix this problem?\n",
+ "1. What is BPTT?\n",
+ "1. Write code to print out the first few batches of the validation set, including converting the token IDs back into English strings, as we showed for batches of IMDb data in the previous chapter.\n",
+ "1. What does the `ModelReseter` callback do? Why do we need it?\n",
+ "1. What are the downsides of predicting just one output word for each three input words?\n",
+ "1. Why do we need a custom loss function for `LMModel4`?\n",
+ "1. Why is the training of `LMModel4` unstable?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. In ` LMModel2` why can `forward` start with `h=0`? Why don't we need to say `h=torch.zeros(…)`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": false,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/12_better_rnn.ipynb b/12_better_rnn.ipynb
new file mode 100644
index 000000000..aa09e48ef
--- /dev/null
+++ b/12_better_rnn.ipynb
@@ -0,0 +1,1157 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "hide_input": false
+ },
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from fastai2.text.all import *\n",
+ "path = untar_data(URLs.HUMAN_NUMBERS)\n",
+ "lines = L()\n",
+ "with open(path/'train.txt') as f: lines += L(*f.readlines())\n",
+ "with open(path/'valid.txt') as f: lines += L(*f.readlines())\n",
+ "text = ' . '.join([l.strip() for l in lines])\n",
+ "tokens = text.split(' ')\n",
+ "vocab = L(*tokens).unique()\n",
+ "word2idx = {w:i for i,w in enumerate(vocab)}\n",
+ "nums = L(word2idx[i] for i in tokens)\n",
+ "\n",
+ "def group_chunks(ds, bs):\n",
+ " m = len(ds) // bs\n",
+ " new_ds = L()\n",
+ " for i in range(m): new_ds += L(ds[i + m*j] for j in range(bs))\n",
+ " return new_ds"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_better_rnn]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Making our RNN state of the art"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We saw in the last chapter how to build a basic RNN from scratch. Now we will see how to make it better up until the AWD LSTM architecture we used in <> on this text classification problem.\n",
+ "\n",
+ "We won't go other the whole data preparation process again. To make the comparison fair against our last example, we use the same batch size and sequence length:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sl,bs = 16,64\n",
+ "seqs = L((tensor(nums[i:i+sl]), tensor(nums[i+1:i+sl+1]))\n",
+ " for i in range(0,len(nums)-sl-1,sl))\n",
+ "cut = int(len(seqs) * 0.8)\n",
+ "dls = DataLoaders.from_dsets(group_chunks(seqs[:cut], bs),\n",
+ " group_chunks(seqs[cut:], bs),\n",
+ " bs=bs, drop_last=True, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The obvious way to get a better model is to go deeper: we only have one linear layer between the hidden state and the output activations in our basic RNN, so maybe we would get better results with more."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multilayer RNNs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In a multilayer RNN, we pass the activations from our recurrent neural network into a second recurrent neural network, like so:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"2-layer](\"images/att_00025.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "…or in an unrolled representation:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"2-layer](\"images/att_00026.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's save some time by using PyTorch's RNN class, which implements exactly what we have created above, but also gives us the option to stack multiple RNNs, as we have discussed:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel5(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden, n_layers):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden)\n",
+ " self.rnn = nn.RNN(n_hidden, n_hidden, n_layers, batch_first=True)\n",
+ " self.h_o = nn.Linear(n_hidden, vocab_sz)\n",
+ " self.h = torch.zeros(n_layers, bs, n_hidden)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " res,h = self.rnn(self.i_h(x), self.h)\n",
+ " self.h = h.detach()\n",
+ " return self.h_o(res)\n",
+ " \n",
+ " def reset(self): self.h.zero_()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = Learner(dls, LMModel5(len(vocab), 64, 2), loss_func=CrossEntropyLossFlat(), metrics=accuracy, cbs=ModelReseter)\n",
+ "learn.fit_one_cycle(15, 3e-3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that's disappointing... we are doing more poorly than the single-layer RNN from the end of last chapter. The reason is that we have a deeper model, leading to exploding or disappearing activations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exploding or disappearing activations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In practice, creating accurate models from this kind of RNN is difficult. We will get better results if we call `detach` less often, and have more layers — this gives our RNN a longer time horizon to learn from, and richer features to create. But it also means we have a deeper model to train. The key challenge in the development of deep learning has been figuring out how to train these kinds of models.\n",
+ "\n",
+ "The reason this is challenging is because of what happens when you multiply by a matrix many times. Think about what happens when you multiply by a number many times. For example, if you multiply by two, starting at one, you get the sequence 1, 2, 4, 8,… after 32 steps you are already at 4,294,967,296. A similar issue happens if we multiply by 0.5: we get 0.5, 0.25, 0.125… and after 32 steps it's 0.00000000023. As you can see, a number even slightly higher or lower than one results in an explosion or disappearance of our number, after just a few repeated multiplications.\n",
+ "\n",
+ "Because matrix multiplication is just multiplying numbers and adding them up, exactly the same thing happens with repeated matrix multiplications. And a deep neural network is just repeated matrix multiplications--each extra layer is another matrix multiplication. This means that it is very easy for a deep neural network to end up with extremely large, or extremely small numbers.\n",
+ "\n",
+ "This is a problem, because the way computers store numbers (known as \"floating point\") means that they become less and less accurate the further away the numbers get from zero. This diagram, from the excellent article [What you never wanted to know about floating point but will be forced to find out](http://www.volkerschatz.com/science/float.html), shows how the precision of floating point numbers varies over the number line:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Precision](\"images/fltscale.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This inaccuracy means that often the gradients calculated for updating the weights end up as zero or infinity for deep networks. This is commonly refered to as *vanishing gradients* or *exploding gradients*. That means that in SGD, the weights are updated either not at all, or jump to infinity. Either way, they won't improve with training.\n",
+ "\n",
+ "Researchers have developed a number of ways to tackle this problem, which we will be discussing later in the book. One way to tackle the problem is to change the definition of a layer in a way that makes it less likely to have exploding activations. We'll look at the details of how this is done in <>, when we discuss *batch normalization*, and <>, when we discuss *ResNets*, although these details don't generally matter in practice (unless you are a researcher that is creating new approaches to solving this problem). Another way to deal with this is by being careful about *initialization*, which is a topic we'll investigate in <>.\n",
+ "\n",
+ "For RNNs, there are two types of layers frequently used to avoid exploding activations, and they are: *gated recurrent units* (GRU), and *Long Short-Term Memory* (LSTM). Both of these are available in PyTorch, and are drop-in replacements for the RNN layer. We will only cover LSTMs in this book, there are plenty of good tutorials online explaining GRUs, which are a minor variant on the LSTM design."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## LSTM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "LSTM (for long short-term memory) is an architecture that was introduced back in 1997 by Jurgen Schmidhuber and Sepp Hochreiter. In this architecture, there are not one but two hidden states. In our base RNN, the hidden state is the output of the RNN at the previous time step. That hidden state is then responsible for doing two things at a time:\n",
+ "\n",
+ "- having the right information for the output layer to predict the correct next token\n",
+ "- retaining memory of everything that happened in the sentence\n",
+ "\n",
+ "Consider, for example, the sentences \"Henry has a dog and he likes his dog very much\" and \"Sophie has a dog and she likes her dog very much\". It's very clear that the RNN needs to remember the name at the beginning of the sentence to be able to predict *he/she* or *his/her*. \n",
+ "\n",
+ "In practice, RNNs are really bad at retaining memory of what happened much earlier in the sentence, which is the motivation to have another hidden state (called cell state) in the LSTM. The cell state will be responsible for keeping *long short-term memory*, while the hidden state will focus on the next token to predict. Let's have a closer look and how this is achieved and build one LSTM from scratch."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Building an LSTM from scratch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The schematic of an LSTM is given like so:\n",
+ "\n",
+ "![\"A](\"images/LSTM.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this picture, our input $x_{t}$ enters on the bottom with the previous hidden state ($h_{t-1}$) and cell state ($x_{t-1}$). The four orange boxes represent four layers with the activation being either sigmoid (for $\\sigma$) or tanh. tanh is just a sigmoid rescaled to the range -1 to 1. Its mathematical expression can be written like this:\n",
+ "\n",
+ "$$\\tanh(x) = \\frac{e^{x} + e^{-x}}{e^{x}-e^{-x}} = 2 \\sigma(2x) - 1$$\n",
+ "\n",
+ "where $\\sigma$ is the sigmoid function. The green boxes are elementwise operations. What goes out is the new hidden state ($h_{t}$) and new cell state ($c_{t}$) on the left, ready for our next input. The new hidden state is also use as output, which is why the arrow splits to go up.\n",
+ "\n",
+ "Let's go over the four neural nets (called *gates*) one by one and explain the diagram, but before this, notice how very little the cell state (on the top) is changed. It doesn't even go directly through a neural net! This is exactly why it will carry on a longer-term state.\n",
+ "\n",
+ "First, the arrows for input and old hidden state are joined together. In the RNN we wrote in the past chapter, we were adding them together. In the LSTM, we stack them in one big tensor. This means the dimension of our embeddings (which is the dimension of $x_{t}$) can be different than the dimension of our hidden state. If we call those `n_in` and `n_hid`, the arrow at the bottom is of size `n_in + n_hid`, thus all the neural nets (orange boxes) are linear layers with `n_in + n_hid` inputs and `n_hid` outputs.\n",
+ "\n",
+ "The first gate (looking from the left to right) is called the *forget gate*. Since it's a linear layer followed by a sigmoid, its output will have scalars between 0 and 1. We multiply this result y the cell gate, so for all the values close to 0, we will forget what was inside that cell state (and for the values close to 1 it doesn't do anything). This gives the ability to the LSTM to forget things about its longterm state. For instance, when crossing a period or an `xxbos` token, we would expect to it to (have learned to) reset its cell state.\n",
+ "\n",
+ "The second gate works is called the *input gate*. It works with the third gate (which doesn't really have a name but is sometimes called the *cell gate*) to update the cell state. For instance we may see a new gender pronoum, so we must replace the information about gender that the forget gate removed by the new one. Like the forget gate, the input gate ends up on a product, so it jsut decides which element of the cell state to update (valeus close to 1) or not (values close to 0). The third gate will then fill those values with things between -1 and 1 (thanks to the tanh). The result is then added to the cell state.\n",
+ "\n",
+ "The last gate is the *output gate*. It will decides which information take in the cell state to generate the output. The cell state goes through a tanh before this and the output gate combined with the sigmoid decides which values to take inside it.\n",
+ "\n",
+ "In terms of code, we can write the same steps like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LSTMCell(Module):\n",
+ " def __init__(self, ni, nh):\n",
+ " self.forget_gate = nn.Linear(ni + nh, nh)\n",
+ " self.input_gate = nn.Linear(ni + nh, nh)\n",
+ " self.cell_gate = nn.Linear(ni + nh, nh)\n",
+ " self.output_gate = nn.Linear(ni + nh, nh)\n",
+ "\n",
+ " def forward(self, input, state):\n",
+ " h,c = state\n",
+ " h = torch.stack([h, input], dim=1)\n",
+ " forget = torch.sigmoid(self.forget_gate(h))\n",
+ " c = c * forget\n",
+ " inp = torch.sigmoid(self.input_gate(h))\n",
+ " cell = torch.tanh(self.cell_gate(h))\n",
+ " c = c + inp * cell\n",
+ " out = torch.sigmoid(self.output_gate(h))\n",
+ " h = outgate * torch.tanh(c)\n",
+ " return h, (h,c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In practice, we can then refactor the code. Also, in terms of performance, it's better to do one big matrix multiplication than four smaller ones (that's because we only launch the special fast kernel on GPU once, and it gives the GPU more work to do in parallel). The stacking takes a bit of time (since we have to move one of the tensors around on the GPU to have it all in a contiguous array), so we use two separate layers for the input and the hidden state. The optimized and refactored code then looks like that:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LSTMCell(Module):\n",
+ " def __init__(self, ni, nh):\n",
+ " self.ih = nn.Linear(ni,4*nh)\n",
+ " self.hh = nn.Linear(nh,4*nh)\n",
+ "\n",
+ " def forward(self, input, state):\n",
+ " h,c = state\n",
+ " #One big multiplication for all the gates is better than 4 smaller ones\n",
+ " gates = (self.ih(input) + self.hh(h)).chunk(4, 1)\n",
+ " ingate,forgetgate,outgate = map(torch.sigmoid, gates[:3])\n",
+ " cellgate = gates[3].tanh()\n",
+ "\n",
+ " c = (forgetgate*c) + (ingate*cellgate)\n",
+ " h = outgate * c.tanh()\n",
+ " return h, (h,c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we use the PyTorch `chunk` method to split our tensor into 4 pieces, e.g.:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t = torch.arange(0,10); t"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([0, 1, 2, 3, 4]), tensor([5, 6, 7, 8, 9]))"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t.chunk(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training a language model using LSTMs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the same network as before, using a two-layer LSTM. We can train it at a higher learning rate, for a shorter time, and get better accuracy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel6(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden, n_layers):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden)\n",
+ " self.rnn = nn.LSTM(n_hidden, n_hidden, n_layers, batch_first=True)\n",
+ " self.h_o = nn.Linear(n_hidden, vocab_sz)\n",
+ " self.h = [torch.zeros(2, bs, n_hidden) for _ in range(n_layers)]\n",
+ " \n",
+ " def forward(self, x):\n",
+ " res,h = self.rnn(self.i_h(x), self.h)\n",
+ " self.h = [h_.detach() for h_ in h]\n",
+ " return self.h_o(res)\n",
+ " \n",
+ " def reset(self): \n",
+ " for h in self.h: h.zero_()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn = Learner(dls, LMModel6(len(vocab), 64, 2), loss_func=CrossEntropyLossFlat(), metrics=accuracy, cbs=ModelReseter)\n",
+ "learn.fit_one_cycle(15, 1e-2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that's better than a multilayer RNN! We can still see there is a bit of overfitting, which is a sign that a bit of regularization might help."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Regularizing an LSTM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Recurrent neural networks, in general, are hard to train. Using LSTMs (or GRUs) cell make training easier than vanilla RNNs, but there are still very prone to overfitting. Data augmentation, while it exists for text data, is less often used because in most cases, it requires another model to generate random augmentation (by translating in another language and back to the language used for instance). Overall, data augmentation for text data is currently not a well explored space.\n",
+ "\n",
+ "However, there are other regularization techniques we can use instead, which were thoroughly studied for use with LSTMs in the paper [Regularizing and Optimizing LSTM Language Models](https://arxiv.org/abs/1708.02182). This paper showed how effective use of *dropout*, *activation regularization*, and *temporal activation regularization* could allow an LSTM to beat state of the art results that previously required much more complicated models. They called an LSTM using these techniques an *AWD LSTM*. We'll look at each of these techniques in turn."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Dropout"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dropout is a regularization technique that was introduce by Geoffrey Hinton et al. in [Improving neural networks by preventing co-adaptation of feature detectors](https://arxiv.org/abs/1207.0580). The basic idea is to randomly change some activations to zero at training time. This makes sure all neurons actively work toward the output as seen in this figure from the original paper:\n",
+ "\n",
+ "![\"A](\"images/Dropout1.png\")
\n",
+ "\n",
+ "Hinton used a nice metaphor when he explained, in an interview, the inspiration for dropout:\n",
+ "\n",
+ "> : \"I went to my bank. The tellers kept changing and I asked one of them why. He said he didn’t know but they got moved around a lot. I figured it must be because it would require cooperation between employees to successfully defraud the bank. This made me realize that randomly removing a different subset of neurons on each example would prevent conspiracies and thus reduce overfitting\"\n",
+ "\n",
+ "In the same interview, he also explained that neuroscience provided additional inspiration:\n",
+ "\n",
+ "> : \"We don't really know why neurons spike. One theory is that they want to be noisy so as to regularize, because we have many more parameters than we have data points. The idea of dropout is that if you have noisy activations, you can afford to use a much bigger model.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see there that if we just zero those activations without doing anything else, our model will have problems to train: if we go from the sum of 5 activations (that are all positive numbers since we apply a ReLU) to just 2, this won't have the same scale. Therefore if we dropout with a probability `p`, we rescale all activation by dividing them by `1-p` (on average `p` will be zeroed, so it leaves `1-p`), as shown in this diagram from the original paper:\n",
+ "\n",
+ "![\"A](\"images/Dropout.png\")
\n",
+ "\n",
+ "This is a full implementation of the dropout layer in PyTorch (although PyTorch's native layer is actually written in C, not Python):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Dropout(Module):\n",
+ " def __init__(self, p): self.p = p\n",
+ " def forward(self, x):\n",
+ " if not self.training: return x\n",
+ " mask = x.new(*x.shape).bernoulli_(1-p)\n",
+ " return x * mask.div_(1-p)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `bernoulli_` method is creating a tensor with random zeros (with probability p) and ones (with probability 1-p), which is then multiplied with our input before dividing by `1-p`. Note the use of the `training` attribute, which is available in any PyTorch `nn.Module`, and tells us if we are doing training or inference.\n",
+ "\n",
+ "> note: In previous chapters of the book we'd be adding a code example for `bernoulli_` here, so you can see exactly how it works. But now that you know enough to do this yourself, we're going to be doing fewer and fewer examples for you, and instead expecting you to do your own experiments to see how things work. In this case, you'll see in the end-of-chapter questionnaire that we're asking you to experiment with `bernoulli_`--but don't wait for us to ask you to experiment to develop your understanding of the code we're studying, go ahead and do it anyway!\n",
+ "\n",
+ "Using dropout before passing the output of our LSTM to the final layer will help reduce overfitting. Dropout is also used in many other models, including the default CNN head used in `fastai.vision`, and is also available in `fastai.tabular` by passing the `ps` parameter (where each \"p\" is passed to each added `Dropout` layer), as we'll see in <>."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dropout has a different behavior in training and validation mode, which we achieved using the `training` attribute in `Dropout` above. Calling the `train()` method on a `Module` sets `training` to `True` (both for the module you call the method on, and for every module it recursively contains), and `eval()` sets it to `False`. This is done automatically when calling the methods of `Learner`, but if you are not using that class, remember to switch from one to the other as needed."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### AR and TAR regularization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "AR (for *activation regularization*) and TAR (for *temporal activation regularization*) are two regularization methods very similar to weight decay. When applying weight decay, we add a small penalty to the loss that aims at making the weights as small as possible. For the activation regularization, it's the final activations produced by the LSTM that we will try to make as small as possible, instead of the weights.\n",
+ "\n",
+ "To regularize the final activations, we have to store those somewhere, then add the means of the squares of them to the loss (along with a multiplier `alpha`, which is just like `wd` for weight decay):\n",
+ "\n",
+ "``` python\n",
+ "loss += alpha * activations.pow(2).mean()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Temporal activation regularization is linked to the fact we are predicting tokens in a sentence. That means it's likely that the outputs of our LSTMs should somewhat make sense when we read them in order. TAR is there to encourage that behavior by adding a penalty to the loss to make the difference between two consecutive activations as small as possible: our activations tensor has a shape `bs x sl x n_hid`, and we read consecutive activation on the sequence length axis (so the dimension in the middle). With this, TAR can be expressed as:\n",
+ "\n",
+ "``` python\n",
+ "loss += beta * (activations[:,1:] - activations[:,:-1]).pow(2).mean()\n",
+ "```\n",
+ "\n",
+ "`alpha` and `beta` are then two hyper-parameters to tune. To make this work, we need our model with dropout to return three things: the proper output, the activations of the LSTM pre-dropout and the activations of the LSTM post-dropout. AR is often applied on the dropped out activations (to not penalize the activations we turned in 0s afterward) while TAR is applied on the non-dropped out activations (because those 0s create big differences between two consecutive timesteps). There is then a callback called `RNNRegularizer` that will apply this regularization for us."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training a weight-tied regularized LSTM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can combine dropout (applied before we go in our output layer) with the AR and TAR regularization to train our previous LSTM. We just need to return three things instead of one: the normal output of our LSTM, the dropped-out activations and the activations from our LSTMs. Those last two will be picked up by the callback `RNNRegularization` for the contributions it has to make to the loss.\n",
+ "\n",
+ "Another useful trick we can add from the AWD LSTM paper is *weight tying*. In a language model, the input embeddings represent a mapping from English words to activations, and the output hidden layer represents a mapping from activations to English words. We might expect, intuitively, that these mappings could be the same. We can represent this in PyTorch by assigning the same weight matrix to each of these layers:\n",
+ "\n",
+ " self.h_o.weight = self.i_h.weight\n",
+ "\n",
+ "In `LMMModel7`, we include these final tweaks:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel7(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden, n_layers, p):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden)\n",
+ " self.rnn = nn.LSTM(n_hidden, n_hidden, n_layers, batch_first=True)\n",
+ " self.drop = nn.Dropout(p)\n",
+ " self.h_o = nn.Linear(n_hidden, vocab_sz)\n",
+ " self.h_o.weight = self.i_h.weight\n",
+ " self.h = [torch.zeros(2, bs, n_hidden) for _ in range(n_layers)]\n",
+ " \n",
+ " def forward(self, x):\n",
+ " raw,h = self.rnn(self.i_h(x), self.h)\n",
+ " out = self.drop(raw)\n",
+ " self.h = [h_.detach() for h_ in h]\n",
+ " return self.h_o(out),raw,out\n",
+ " \n",
+ " def reset(self): \n",
+ " for h in self.h: h.zero_()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create a regularized `Learner` using the `RNNRegularizer` callback:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = Learner(dls, LMModel7(len(vocab), 64, 2, 0.5),\n",
+ " loss_func=CrossEntropyLossFlat(), metrics=accuracy,\n",
+ " cbs=[ModelReseter, RNNRegularizer(alpha=2, beta=1)])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A `TextLearner` automatically adds those two callbacks for us (with default for `alpha` and `beta` as above) so we can simplify the line above to:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = TextLearner(dls, LMModel7(len(vocab), 64, 2, 0.4),\n",
+ " loss_func=CrossEntropyLossFlat(), metrics=accuracy)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can the train the model, and add additional regularization by increasing the weight decay to `0.1`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(15, 1e-2, wd=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now this is far better than our previous model!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusion"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You have now seen everything that is inside the AWD-LSTM architecture we used in text classification in <>. It uses dropouts in a lot more places:\n",
+ "\n",
+ "- embedding dropout (just after the embedding layer)\n",
+ "- input dropout (after the embedding layer)\n",
+ "- weight dropout (applied to the weights of the LSTM at each training step)\n",
+ "- hidden dropout (applied to the hidden state between two layers)\n",
+ "\n",
+ "which makes it even more regularized. Since fine-tuning those five dropout values (adding the dropout before the output layer) is complicated, so we have determined good defaults, and allow the magnitude of dropout to be tuned overall with the `drop_mult` parameter you saw (which is multiplied by each dropout).\n",
+ "\n",
+ "Another architecture that is very powerful, especially in \"sequence to sequence\" problems (that is, problems where the dependent variable is itself a variable length sequence, such as language translation), is the Transformers architecture. You can find it in an online bonus chapter on the book website."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. In the unrolled representation, we can see that a recurrent neural network actually has many layers. So why do we need to stack RNNs to get better results?\n",
+ "1. Draw a representation of a stacked (multilayer) RNN.\n",
+ "1. Why should we get better results in an RNN if we call `detach` less often? Why might this not happen in practice with a simple RNN?\n",
+ "1. Why can a deep network result in very large or very small activations? Why does this matter?\n",
+ "1. In a computer's floating point representation of numbers, which numbers are the most precise?\n",
+ "1. Why do vanishing gradients prevent training?\n",
+ "1. Why does it help to have two hidden states in the LSTM architecture? What is the purpose of each one?\n",
+ "1. What are these two states called in an LSTM?\n",
+ "1. What is tanh, and how is it related to sigmoid?\n",
+ "1. What is the purpose of this code in `LSTMCell`?: `h = torch.stack([h, input], dim=1)`\n",
+ "1. What does `chunk` to in PyTorch?\n",
+ "1. Study the refactored version of `LSTMCell` carefully to ensure you understand how and why it does the same thing as the non-refactored version.\n",
+ "1. Why can we use a higher learning rate for `LMModel6`?\n",
+ "1. What are the three regularisation techniques used in an AWD-LSTM model?\n",
+ "1. What is dropout?\n",
+ "1. Why do we scale the weights with dropout? Is this applied during training, inference, or both?\n",
+ "1. What is the purpose of this line from `Dropout`?: `if not self.training: return x`\n",
+ "1. Experiment with `bernoulli_` to understand how it works.\n",
+ "1. How do you set your model in training mode in PyTorch? In evaluation mode?\n",
+ "1. Write the equation for activation regularization (in maths or code, as you prefer). How is it different to weight decay?\n",
+ "1. Write the equation for temporal activation regularization (in maths or code, as you prefer). Why wouldn't we use this for computer vision problems?\n",
+ "1. What is \"weight tying\" in a language model?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Write the code for an LSTM from scratch (but you may refer to <>).\n",
+ "1. Search on the Internet for the GRU architecture and implement it from scratch, and try training a model. See if you can get the similar results as we saw in this chapter. Compare it to the results of PyTorch's built in GRU module.\n",
+ "1. Have a look at the source code for AWD-LSTM in fastai, and try to map each of the lines of code to the concepts shown in this chapter."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
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+ "split_at_heading": true
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diff --git a/13_convolutions.ipynb b/13_convolutions.ipynb
new file mode 100644
index 000000000..50b14bcd3
--- /dev/null
+++ b/13_convolutions.ipynb
@@ -0,0 +1,2726 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from fastai2.vision.all import *\n",
+ "from utils import *\n",
+ "\n",
+ "matplotlib.rc('image', cmap='Greys')"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_convolutions]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Convolutional neural networks"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The magic of convolutions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In <> we learned how to create a neural network recognising images. We were able to achieve a bit over 98% accuracy at recognising threes from sevens. But we also saw that fastai's built in classes were able to get close to 100%. Let's start trying to close the gap."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = untar_data(URLs.MNIST_SAMPLE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the most powerful tools that machine learning practitioners have at their disposal is *feature engineering*. A *feature* is a transformation of the data which is designed to make it easier to model. For instance, the `add_datepart` function that we used for our tabular data set preprocessing added date features to the Bulldozers dataset. What kind of features might we be able to create from images?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: Feature engineering: creating new transformations of the input data in order to make it easier to model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the context of an image, a *feature* will be a visually distinctive attribute of an image. Here's an idea: the number seven is characterised by a horizontal edge near the top of the digit, and a bottom left to top right diagonal edge underneath that. On the other hand, the number three is characterised by a diagonal edge in one direction in the top left and bottom right of the digit, the opposite diagonal on the bottom left and top right, a horizontal edge in the middle of the top and the bottom, and so forth. So what if we could extract information about where the edges occur in each image, and then use that as our features, instead of raw pixels?\n",
+ "\n",
+ "It turns out that finding the edges in an image is a very common task in computer vision, and is surprisingly straightforward. To do it, we use something called a *convolution*. A convolution requires nothing more than multiplication, and addition — two operations which are responsible for the vast majority of work that we will see in every single deep learning model in this book!\n",
+ "\n",
+ "A convolution applies a *kernel* across an image. A kernel is a little matrix, such as the 3x3 matrix in the top right of this image:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Applying](\"images/chapter9_conv_basic.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The grey grid to the left is our *image* we're going to apply the kernel to. The convolution operation multiplies each element of the kernel, to each element of a 3x3 block of the image. The results of these multiplications are then added together. The diagram above shows an example of applying a kernel to a single location in the image, the 3x3 block around cell 18.\n",
+ "\n",
+ "Let's do this with code. First, we create a little 3x3 matrix like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "top_edge = tensor([[-1,-1,-1],\n",
+ " [ 0, 0, 0],\n",
+ " [ 1, 1, 1]]).float()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're going to call this our *kernel*\n",
+ "(because that's what fancy computer vision researchers call these). And we'll need an image, of course:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEQAAABECAYAAAA4E5OyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAADyElEQVR4nO2aTSg1URjHf1eIS9gQETYWPhOiFLGwkiTJzs7OXpFsWMlKsqEoRT4WFmKhlI+wsWWluCtECIVh3oX3mNd5hzvGneum51d3MzPOee7//p3zPM8Zn2maCBZRPx1ApCGCaIggGiKIhgiiER3k/m/egnx2F8UhGiKIhgiiIYJoiCAaIoiGCKIhgmiIIBrBMlVbHh8fAVhfXwcgPj4egO3tbQCur68BGBkZAaClpQWArKysD8fMzMwEoLm5GYDs7Gw3oX0bcYiGL0jHzPbm0NAQAN3d3SEPKCrq9TeqqKgAoLOzE4DW1lYAUlJSQjWV1DJOcOWQgoICAA4PD23/KC0tDYCamppPJ8/Pzwfg4OCAs7MzADY3N22f3d/fB6C0tPTTMb+AOMQJrnaZra0tAE5OToD/d4TY2FgAEhMTHY/58PAAQGFhIQBHR0fv7s/PzwMhdYgt4hANV2uIF2xsbABQV1f37npcXBzwus4A5OTkhGpKWUMcYZrmZx9PMQzDNAzD7O3tNf1+v+n3+02fz/fuEwgEzEAg4MX0tt9ZHKLhapf5Lip/mZiYAGB4ePjtXkxMDACLi4sApKenhzU2cYhGWB1yfHwMQHFxMQDPz8//PaNqGVUZ+3y2m4FniEM0wuqQ2dlZwN4ZCpWxlpWVAVBfXw9Ae3s7AE1NTQBkZGR4EmNYEzOVjvf39wOwtrYGwOnpqeMx1L/U4OAgAF1dXQAkJCR8NRxJzJzwo6m7ajXe3NxweXkJwMzMDGA1oYLE99aeXFhYAL60CItDnBAxxZ2OKvYGBgYAa735iMnJSQA6OjqcTiEOccKPpO5OqK2tBWB1dRWwmsxLS0u2z6v2wHcRh2hErEMUKu+oqqoCPnZIUVFRaOYLySi/CE8dcnt7C8D09DQAJSUlAFRXVzse4+XlBbCOIXSio1+/QmVlpes4/0UcouGJQ5QzGhoaANjb2wPg/v7e8Rh3d3cAjI2NAVYmqlNeXg5AXl6eu2A1xCEanjhEHYIrZyguLi4A66hTtQsBnp6eABgfHwegp6cHsOodhcqsk5OTAZiamgpp7OIQDU9qmZWVFQAaGxtt76tD8NTU1Ldr5+fnwMeH3YqkpCQAdnZ2AOvA3AVSyzjBE4dcXV0B0NfXB8Do6KibYQArz1Adsra2NgByc3Ndj/kXcYgTPO2HGIYBwO7uLgDLy8uAVXfMzc29PatewlGo9Uc54bMX9lwiDnFCxHbMwoA4xAkiiIYIoiGCaIggGsGq3fC+ixABiEM0RBANEURDBNEQQTREEI0/H3jyQ4wdtXsAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "time | \n", + "
|---|---|---|---|
| 0 | \n", + "0.069705 | \n", + "0.062389 | \n", + "00:11 | \n", + "
| 1 | \n", + "0.056253 | \n", + "0.058489 | \n", + "00:11 | \n", + "
| 2 | \n", + "0.048385 | \n", + "0.052256 | \n", + "00:11 | \n", + "
| 3 | \n", + "0.043400 | \n", + "0.050743 | \n", + "00:11 | \n", + "
| 4 | \n", + "0.040358 | \n", + "0.050986 | \n", + "00:11 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is showing the mean average percent error (MAPE) compared amongst four different modelling techniques, three of which we have already seen, along with \"KNN\" (K nearest neighbours), which is a very simple baseline method. The first numeric column contains the results using just the methods on the data provided in the competition; the second column shows what happens if you first train a neural network with categorical embeddings, and then use those categorical embeddings instead of the raw categorical columns in the model. As you see, in every case, the models are dramatically improved by using the embeddings, instead of the raw category.\n",
+ "\n",
+ "This is a really important result, because it shows that you can get much of the performance improvement of a neural network, without actually having to use a neural network at all at inference time. You could just use an embedding, which is literally just an array lookup, along with a small decision tree ensemble.\n",
+ "\n",
+ "These embeddings need not even be necessarily learned separately for each model or task in an organisation. Instead, once a set of embeddings are learned for some column for some task, they could be stored in a central place, and reused across multiple models. In fact, we know from private communication with other practitioners at large companies that this is already happening in many places."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Our advice for tabular modeling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have two approaches to tabular modelling: decision tree ensembles, and neural networks. And we have mentioned two different decision tree ensembles: random forests, and gradient boosting machines. Each is very effective, but each also has compromises:\n",
+ "\n",
+ "**Random forests** are the easiest to train, because they are extremely resilient to hyperparameter choices, and require very little preprocessing. They are very fast to train, and should not overfit, if you have enough trees. But, they can be a little less accurate, especially if extrapolation is required, such as predicting future time periods\n",
+ "\n",
+ "**Gradient boosting machines** in theory are just as fast to train as random forests, but in practice you will have to try lots of different hyperparameters. They can overfit. But they are often a little bit more accurate than random forests.\n",
+ "\n",
+ "**Neural networks** take the longest time to train, and require extra preprocessing such as normalisation; this normalisation needs to be used at inference time as well. They can provide great results, and extrapolate well, but only if you are careful with your hyperparameters, and are careful to avoid overfitting.\n",
+ "\n",
+ "We suggest starting your analysis with a random forest. This will give you a strong baseline, and you can be confident that it's a reasonable starting point. You can then use that model for feature selection and partial dependence analysis, to get a better understanding of your data.\n",
+ "\n",
+ "From that foundation, you can try neural nets and GBMs, and if they give you significantly better results on your validation set in a reasonable amount of time, you can use them. If decision tree ensembles are working well for you, try adding the embeddings for the categorical variables to the data, and see if that helps your decision trees learn better."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What is a continuous variable?\n",
+ "1. What is a categorical variable?\n",
+ "1. Provide 2 of the words that are used for the possible values of a categorical variable.\n",
+ "1. What is a \"dense layer\"?\n",
+ "1. How do entity embeddings reduce memory usage and speed up neural networks?\n",
+ "1. What kind of datasets are entity embeddings especially useful for?\n",
+ "1. What are the two main families of machine learning algorithms?\n",
+ "1. Why do some categorical columns need a special ordering in their classes? How do you do this in pandas?\n",
+ "1. Summarize what a decision tree algorithm does.\n",
+ "1. Why is a date different from a regular categorical or continuous variable, and how can you preprocess it to allow it to be used in a model?\n",
+ "1. Should you pick a random validation set in the bulldozer competition? If no, what kind of validation set should you pick?\n",
+ "1. What is pickle and what is it useful for?\n",
+ "1. How are `mse`, `samples`, and `values` calculated in the decision tree drawn in this chapter?\n",
+ "1. How do we deal with outliers, before building a decision tree?\n",
+ "1. How do we handle categorical variables in a decision tree?\n",
+ "1. What is bagging?\n",
+ "1. What is the difference between `max_samples` and `max_features` when creating a random forest?\n",
+ "1. If you increase `n_estimators` to a very high value, can that lead to overfitting? Why or why not?\n",
+ "1. What is *out of bag error*?\n",
+ "1. Make a list of reasons why a model's validation set error might be worse than the OOB error. How could you test your hypotheses?\n",
+ "1. How can you answer each of these things with a random forest? How do they work?:\n",
+ " - How confident are we in our projections using a particular row of data?\n",
+ " - For predicting with a particular row of data, what were the most important factors, and how did they influence that prediction?\n",
+ " - Which columns are the strongest predictors?\n",
+ " - How do predictions vary, as we vary these columns?\n",
+ "1. What's the purpose of removing unimportant variables?\n",
+ "1. What's a good type of plot for showing tree interpreter results?\n",
+ "1. What is the *extrapolation problem*?\n",
+ "1. How can you tell if your test or validation set is distributed in a different way to your training set?\n",
+ "1. Why do we make `saleElapsed` a continuous variable, even although it has less than 9000 distinct values?\n",
+ "1. What is boosting?\n",
+ "1. How could we use embeddings with a random forest? Would we expect this to help?\n",
+ "1. Why might we not always use a neural net for tabular modeling?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Pick a competition on Kaggle with tabular data (current or past) and try to adapt the techniques seen in this chapter to get the best possible results. Compare yourself to the private leaderboard.\n",
+ "1. Implement the decision tree algorithm in this chapter from scratch yourself, and try it on this dataset.\n",
+ "1. Use the embeddings from the neural net in this chapter in a random forest, and see if you can improve on the random forest results we saw.\n",
+ "1. Explain what each line of the source of `TabularModel` does (with the exception of the `BatchNorm1d` and `Dropout` layers)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/10_nlp.ipynb b/10_nlp.ipynb
new file mode 100644
index 000000000..58f04e194
--- /dev/null
+++ b/10_nlp.ipynb
@@ -0,0 +1,2360 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *\n",
+ "from IPython.display import display,HTML"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_nlp]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NLP deep dive: RNNs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In <"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Language model fine tuning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A *language model* is a model that learns to predict the next word of a sentence. Have a think about how you would turn this language modelling problem into a neural network, given what you have learned so far. We'll be able to use concepts that we've seen in the last two chapters.\n",
+ "\n",
+ "It's not at all obvious how we're going to use what we've learned so far to build a language model. Sentences can be different lengths, and documents can be very long. So, how can we predict the next word of a sentence using a neural network? Let's find out!\n",
+ "\n",
+ "We've already seen how categorical variables can be used as independent variables for a neural network. The approach we took for a single categorical variable was to:\n",
+ "\n",
+ "1. Make a list of all possible levels of that categorical variable (let us call this list the *vocab*)\n",
+ "1. Replace each level with its index in the vocab\n",
+ "1. Create an embedding matrix for this containing a row for each level (i..e, for each item of the vocab)\n",
+ "1. Use this embedding matrix as the first layer of a neural network. (A dedicated embedding matrix can take as inputs the raw vocab indexes created in step two; this is equivalent to, but faster and more efficient, than a matrix which takes as input one-hot encoded vectors representing the indexes)\n",
+ "\n",
+ "We can do nearly the same thing with text! What is new is the idea of a sequence. First we concatenate all of the documents in our dataset into one big long string and split it into words, giving us a very long list of words. Our independent variable will be the sequence of words starting with the first word in our very long list and ending with the second last, and our dependent variable would be the sequence of words starting with the second word and ending with the last word. \n",
+ "\n",
+ "When creating our vocab, we will have very common words that will probably be in the vocabulary of our pretrained model, but we will also have new words specific to our corpus (cinematographic terms, or actor names for instance). Our embedding matrix will be built accordingly: for words that are in the vocabulary of our pretrained model, we will take the corresponding row in the embedding matrix of this pretrained model; but for new words, we won't have anything, so we will just initialize the corresponding row with a random vector."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each of the steps necessary to create a language model has jargon associated with it from the world of natural language processing, and fastai and PyTorch classes available to help. The steps are:\n",
+ "\n",
+ "- **Tokenization**: convert the text into a list of words (or characters, or substrings, depending on the granularity of your model)\n",
+ "- **Numericalization**: make a list of all of the unique words which appear (the vocab), and convert each word into a number, by looking up its index in the vocab\n",
+ "- **Language model data loader** creation: fastai provides an `LMDataLoader` class which automatically handles creating a dependent variable which is offset from the independent variable buy one token. It also handles some important details, such as how to shuffle the training data in such a way that the dependent and independent variables maintain their structure as required\n",
+ "- **Language model** creation: we need a special kind of model which does something we haven't seen before: handles input lists which could be arbitrarily big or small. There are a number of ways to do this; in this chapter we will be using a *recurrent neural network*. We will get to the details of this in the <| xxbos | \n", + "xxmaj | \n", + "in | \n", + "this | \n", + "chapter | \n", + ", | \n", + "we | \n", + "will | \n", + "go | \n", + "back | \n", + "over | \n", + "the | \n", + "example | \n", + "of | \n", + "classifying | \n", + "
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| will | \n", + "study | \n", + "how | \n", + "we | \n", + "build | \n", + "a | \n", + "language | \n", + "model | \n", + "and | \n", + "train | \n", + "it | \n", + "for | \n", + "a | \n", + "while | \n", + ". | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In a perfect world, we could then give this one batch to our model. But that doesn't work, because this would very likely not fit in our GPU memory (here we have 90 tokens, but all the IMDb reviews together give several millions of tokens).\n",
+ "\n",
+ "So in fact we will need to divide this array more finely into subarrays of a fixed sequence length. It is important to maintain order within and across these subarrays, because we will use a model that maintains state in order so that it remembers what it read previously when predicting what comes next. \n",
+ "\n",
+ "Going back to our previous example with 6 batches of length 15, if we chose sequence length of 5, that would mean we first feed the following array:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hide_input": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| over | \n", + "the | \n", + "example | \n", + "of | \n", + "classifying | \n", + "
| under | \n", + "the | \n", + "surface | \n", + ". | \n", + "xxmaj | \n", + "
| convert | \n", + "text | \n", + "into | \n", + "numbers | \n", + "and | \n", + "
| we | \n", + "'ll | \n", + "have | \n", + "another | \n", + "example | \n", + "
| . | \n", + "\\n | \n", + "xxmaj | \n", + "then | \n", + "we | \n", + "
| it | \n", + "for | \n", + "a | \n", + "while | \n", + ". | \n", + "
| \n", + " | text | \n", + "text_ | \n", + "
|---|---|---|
| 0 | \n", + "xxbos xxmaj it 's awesome ! xxmaj in xxmaj story xxmaj mode , your going from punk to pro . xxmaj you have to complete goals that involve skating , driving , and walking . xxmaj you create your own skater and give it a name , and you can make it look stupid or realistic . xxmaj you are with your friend xxmaj eric throughout the game until he betrays you and gets you kicked off of the skateboard | \n", + "xxmaj it 's awesome ! xxmaj in xxmaj story xxmaj mode , your going from punk to pro . xxmaj you have to complete goals that involve skating , driving , and walking . xxmaj you create your own skater and give it a name , and you can make it look stupid or realistic . xxmaj you are with your friend xxmaj eric throughout the game until he betrays you and gets you kicked off of the skateboard xxunk | \n", + "
| 1 | \n", + "what xxmaj i 've read , xxmaj death xxmaj bed is based on an actual dream , xxmaj george xxmaj barry , the director , successfully transferred dream to film , only a genius could accomplish such a task . \\n\\n xxmaj old mansions make for good quality horror , as do portraits , not sure what to make of the killer bed with its killer yellow liquid , quite a bizarre dream , indeed . xxmaj also , this | \n", + "xxmaj i 've read , xxmaj death xxmaj bed is based on an actual dream , xxmaj george xxmaj barry , the director , successfully transferred dream to film , only a genius could accomplish such a task . \\n\\n xxmaj old mansions make for good quality horror , as do portraits , not sure what to make of the killer bed with its killer yellow liquid , quite a bizarre dream , indeed . xxmaj also , this is | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "hidden": true
+ },
+ "source": [
+ "It takes quite a while to train each epoch, so we'll be saving the intermediate model results during the training process. Since `fine_tune` doesn't do that for us, we'll just use `fit_one_cycle`. Just like `cnn_learner`, `language_model_learner` automatically calls `freeze` when using a pretrained model (which is the default), so this will only train the embeddings (which is the only part of the model that contains randomly initialized weights--i.e. embeddings for words that are in our IMDb vocab, but aren't in the pretrained model vocab):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "hidden": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "perplexity | \n", + "time | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "4.120048 | \n", + "3.912788 | \n", + "0.299565 | \n", + "50.038246 | \n", + "11:39 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "perplexity | \n", + "time | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "3.893486 | \n", + "3.772820 | \n", + "0.317104 | \n", + "43.502548 | \n", + "12:37 | \n", + "
| 1 | \n", + "3.820479 | \n", + "3.717197 | \n", + "0.323790 | \n", + "41.148880 | \n", + "12:30 | \n", + "
| 2 | \n", + "3.735622 | \n", + "3.659760 | \n", + "0.330321 | \n", + "38.851997 | \n", + "12:09 | \n", + "
| 3 | \n", + "3.677086 | \n", + "3.624794 | \n", + "0.333960 | \n", + "37.516987 | \n", + "12:12 | \n", + "
| 4 | \n", + "3.636646 | \n", + "3.601300 | \n", + "0.337017 | \n", + "36.645859 | \n", + "12:05 | \n", + "
| 5 | \n", + "3.553636 | \n", + "3.584241 | \n", + "0.339355 | \n", + "36.026001 | \n", + "12:04 | \n", + "
| 6 | \n", + "3.507634 | \n", + "3.571892 | \n", + "0.341353 | \n", + "35.583862 | \n", + "12:08 | \n", + "
| 7 | \n", + "3.444101 | \n", + "3.565988 | \n", + "0.342194 | \n", + "35.374371 | \n", + "12:08 | \n", + "
| 8 | \n", + "3.398597 | \n", + "3.566283 | \n", + "0.342647 | \n", + "35.384815 | \n", + "12:11 | \n", + "
| 9 | \n", + "3.375563 | \n", + "3.568166 | \n", + "0.342528 | \n", + "35.451500 | \n", + "12:05 | \n", + "
| \n", + " | text | \n", + "category | \n", + "
|---|---|---|
| 0 | \n", + "xxbos i rate this movie with 3 skulls , only coz the girls knew how to scream , this could 've been a better movie , if actors were better , the twins were xxup ok , i believed they were evil , but the eldest and youngest brother , they sucked really bad , it seemed like they were reading the scripts instead of acting them … . spoiler : if they 're vampire 's why do they freeze the blood ? vampires ca n't drink frozen blood , the sister in the movie says let 's drink her while she is alive … .but then when they 're moving to another house , they take on a cooler they 're frozen blood . end of spoiler \\n\\n it was a huge waste of time , and that made me mad coz i read all the reviews of how | \n", + "neg | \n", + "
| 1 | \n", + "xxbos i have read all of the xxmaj love xxmaj come xxmaj softly books . xxmaj knowing full well that movies can not use all aspects of the book , but generally they at least have the main point of the book . i was highly disappointed in this movie . xxmaj the only thing that they have in this movie that is in the book is that xxmaj missy 's father comes to xxunk in the book both parents come ) . xxmaj that is all . xxmaj the story line was so twisted and far fetch and yes , sad , from the book , that i just could n't enjoy it . xxmaj even if i did n't read the book it was too sad . i do know that xxmaj pioneer life was rough , but the whole movie was a downer . xxmaj the rating | \n", + "neg | \n", + "
| 2 | \n", + "xxbos xxmaj this , for lack of a better term , movie is lousy . xxmaj where do i start … … \\n\\n xxmaj cinemaphotography - xxmaj this was , perhaps , the worst xxmaj i 've seen this year . xxmaj it looked like the camera was being tossed from camera man to camera man . xxmaj maybe they only had one camera . xxmaj it gives you the sensation of being a volleyball . \\n\\n xxmaj there are a bunch of scenes , haphazardly , thrown in with no continuity at all . xxmaj when they did the ' split screen ' , it was absurd . xxmaj everything was squished flat , it looked ridiculous . \\n\\n xxmaj the color tones were way off . xxmaj these people need to learn how to balance a camera . xxmaj this ' movie ' is poorly made , and | \n", + "neg | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.347427 | \n", + "0.184480 | \n", + "0.929320 | \n", + "00:33 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.247763 | \n", + "0.171683 | \n", + "0.934640 | \n", + "00:37 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.193377 | \n", + "0.156696 | \n", + "0.941200 | \n", + "00:45 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.172888 | \n", + "0.153770 | \n", + "0.943120 | \n", + "01:01 | \n", + "
| 1 | \n", + "0.161492 | \n", + "0.155567 | \n", + "0.942640 | \n", + "00:57 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Kao estimated that \"less than 800,000 of the 22M+ comments… could be considered truly unique\" and that \"more than 99% of the truly unique comments were in favor of keeping net neutrality.\"\n",
+ "\n",
+ "Given advances in language modeling that have occurred since 2017, such fraudulent campaigns could be nearly impossible to catch now. You now have all the tools at your disposal necessary to create and compelling language model. That is, something that can generate context appropriate believable text. It won't necessarily be perfectly accurate or correct, but it will be believable. Think about what this technology would mean when put together with the kinds of disinformation campaigns we have learned about. Take a look at this conversation on Reddit, where a language model based on OpenAI's GPT-2 algorithm is having a conversation with itself about whether the US government should cut defense spending:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, the use of the algorithm is being done explicitly. But imagine what would happen if a bad actor decided to release such an algorithm across social networks. They could do it slowly and carefully, allowing the algorithms to gradually develop followings and trust over time. It would not take many resources to have literally millions of accounts doing this. In such a situation we could easily imagine it getting to a point where the vast majority of discourse online was from bots, and nobody would have any idea that it was happening.\n",
+ "\n",
+ "We are already starting to see examples of machine learning being used to generate identities. For example, here is the LinkedIn profile for Katie Jones:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Katie Jones was connected on LinkedIn to several members of mainstream Washington think tanks. But she didn't exist. That image you see is auto generated by a generative adversarial network, and somebody named Katie Jones has not, in fact, graduated from the Centre for Strategic and International Studies.\n",
+ "\n",
+ "Many people assume or hope that algorithms will come to our defence here. The hope is that we will develop classification algorithms which can automatically recognise auto generated content. The problem, however, is that this will always be an arms race, in which better classification (or discriminator) algorithms can be used to create better generation algorithms."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What is self-supervised learning?\n",
+ "1. What is a language model?\n",
+ "1. Why is a language model considered self-supervised learning?\n",
+ "1. What are self-supervised models usually used for?\n",
+ "1. What do we fine-tune language models?\n",
+ "1. What are the three steps to create a state-of-the-art text classifier?\n",
+ "1. How do the 50,000 unlabeled movie reviews help create a better text classifier for the IMDb dataset?\n",
+ "1. What are the three steps to prepare your data for a language model?\n",
+ "1. What is tokenization? Why do we need it?\n",
+ "1. Name three different approaches to tokenization.\n",
+ "1. What is 'xxbos'?\n",
+ "1. List 4 rules that fastai applies to text during tokenization.\n",
+ "1. Why are repeated characters replaced with a token showing the number of repetitions, and the character that's repeated?\n",
+ "1. What is numericalization?\n",
+ "1. Why might there be words that are replaced with the \"unknown word\" token?\n",
+ "1. With a batch size of 64, the first row of the tensor representing the first batch contains the first 64 tokens for the dataset. What does the second row of that tensor contain? What does the first row of the second batch contain? (Careful—students often get this one wrong! Be sure to check your answer against the book website.)\n",
+ "1. Why do we need padding for text classification? Why don't we need it for language modeling?\n",
+ "1. What does an embedding matrix for NLP contain? What is its shape?\n",
+ "1. What is perplexity?\n",
+ "1. Why do we have to pass the vocabulary of the language model to the classifier data block?\n",
+ "1. What is gradual unfreezing?\n",
+ "1. Why is text generation always likely to be ahead of automatic identification of machine generated texts?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. See what you can learn about language models and disinformation. What are the best language models today? Have a look at some of their outputs. Do you find them convincing? How could a bad actor best use this to create conflict and uncertainty?\n",
+ "1. Given the limitation that models are unlikely to be able to consistently recognise machine generated texts, what other approaches may be needed to handle large-scale disinformation campaigns that leveraged deep learning?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Becoming a deep learning practitioner"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Congratulations — you've completed all of the chapters in this book which cover the key practical parts of training and using deep learning! You know how to use all of fastai's built in applications, and how to customise them using the data blocks API and loss functions. You even know how to create a neural network from scratch, and train it! (And hopefully you now know some of the questions to ask to help make sure your creations help improve society too.)\n",
+ "\n",
+ "The knowledge you already have is enough to create full working prototypes of many types of neural network application. More importantly, it will help you understand the capabilities and limitations of deep learning models, and how to design a system which best handles these capabilities and limitations.\n",
+ "\n",
+ "In the rest of this book we will be pulling apart these applications, piece by piece, to understand all of the foundations they are built on. This is important knowledge for a deep learning practitioner, because it is the knowledge which allows you to inspect and debug models that you build, and to create new applications which are customised for your particular projects."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {
+ "height": "367.997px",
+ "width": "278.999px"
+ },
+ "number_sections": false,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/11_nlp_dive.ipynb b/11_nlp_dive.ipynb
new file mode 100644
index 000000000..73a197643
--- /dev/null
+++ b/11_nlp_dive.ipynb
@@ -0,0 +1,1313 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_nlp_dive]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A language model from scratch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're now ready to go deep... deep into deep learning! You already learned how to train a basic neural network, but how do you go from there to creating state of the art models? In this part of the book we're going to uncover all of the mysteries, starting with language models."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Whenever we start working on a new problem, we always first try to think of the simplest dataset we can which would allow us to try out methods quickly and easily, and interpret the results. When we started working on language modelling a few years ago, we didn't find any datasets that would allow for quick prototyping, so we made one. We call it *human numbers*, and it simply contains the first 10,000 words written out in English."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: One of the most common practical mistakes I see even amongst highly experienced practitioners is failing to use appropriate datasets at appropriate times during the analysis process. In particular, most people tend to start with datasets which are too big and too complicated."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can download, extract, and take a look at our dataset in the usual way:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.text.all import *\n",
+ "path = untar_data(URLs.HUMAN_NUMBERS)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#2) [Path('train.txt'),Path('valid.txt')]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's open those two files and see what's inside. At first we'll join all of those texts together and ignore the split train/valid given by the dataset, we will come back to it later on:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#9998) ['one \\n','two \\n','three \\n','four \\n','five \\n','six \\n','seven \\n','eight \\n','nine \\n','ten \\n'...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lines = L()\n",
+ "with open(path/'train.txt') as f: lines += L(*f.readlines())\n",
+ "with open(path/'valid.txt') as f: lines += L(*f.readlines())\n",
+ "lines"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We take all those lines and concatenate them in one big stream. To mark when we go from one number to the next, we use a '.' as separation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'one . two . three . four . five . six . seven . eight . nine . ten . eleven . twelve . thirteen . fo'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text = ' . '.join([l.strip() for l in lines])\n",
+ "text[:100]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's use word tokenization for this dataset, by splitting on spaces:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['one', '.', 'two', '.', 'three', '.', 'four', '.', 'five', '.']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokens = text.split(' ')\n",
+ "tokens[:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To numericalize, we have to create a list of all the unique tokens (our *vocab*):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#30) ['one','.','two','three','four','five','six','seven','eight','nine'...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab = L(*tokens).unique()\n",
+ "vocab"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we can convert our tokens into numbers by looking up the index of each in the vocab:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#63095) [0,1,2,1,3,1,4,1,5,1...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "word2idx = {w:i for i,w in enumerate(vocab)}\n",
+ "nums = L(word2idx[i] for i in tokens)\n",
+ "nums"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Our first language model from scratch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One simple way to turn this into a neural network would be to specify that we are going to predict each word based on the previous three words. Therefore, we could create a list of every sequence of three words as independent variables, and the next word after each sequence as the dependent variable. \n",
+ "\n",
+ "We can do that with plain Python. Let us do it first with tokens just to confirm what it looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#21031) [(['one', '.', 'two'], '.'),(['.', 'three', '.'], 'four'),(['four', '.', 'five'], '.'),(['.', 'six', '.'], 'seven'),(['seven', '.', 'eight'], '.'),(['.', 'nine', '.'], 'ten'),(['ten', '.', 'eleven'], '.'),(['.', 'twelve', '.'], 'thirteen'),(['thirteen', '.', 'fourteen'], '.'),(['.', 'fifteen', '.'], 'sixteen')...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "L((tokens[i:i+3], tokens[i+3]) for i in range(0,len(tokens)-4,3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we will do it with tensors of the numericalized values, which is what the model will actually use:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#21031) [(tensor([0, 1, 2]), 1),(tensor([1, 3, 1]), 4),(tensor([4, 1, 5]), 1),(tensor([1, 6, 1]), 7),(tensor([7, 1, 8]), 1),(tensor([1, 9, 1]), 10),(tensor([10, 1, 11]), 1),(tensor([ 1, 12, 1]), 13),(tensor([13, 1, 14]), 1),(tensor([ 1, 15, 1]), 16)...]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "seqs = L((tensor(nums[i:i+3]), nums[i+3]) for i in range(0,len(nums)-4,3))\n",
+ "seqs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we can batch those easily using the `DataLoader` class. For now we will split randomly the sequences."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bs = 64\n",
+ "cut = int(len(seqs) * 0.8)\n",
+ "dls = DataLoaders.from_dsets(seqs[:cut], seqs[cut:], bs=64, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create a neural network architecture that takes three words as input, and returns a prediction of the probability of each possible next word in the vocab. We will use three standard linear layers, but with two tweaks.\n",
+ "\n",
+ "The first tweak is that the first linear layer will use only the first word's embedding as activations, the second layer will use the second word's embedding plus the first layer's output activations, and the third layer will use the third word's embedding plus the second layer's output activations. The key effect of this is that every word is interpreted in the information context of any words preceding it. \n",
+ "\n",
+ "The second tweak is that each of these three layers will use the same weight matrix. The way that one word impacts the activations from previous words should not change depending on the position of a word. In other words, activation values will change as data moves through the layers, but the layer weights themselves will not change from layer to layer. So a layer does not learn one sequence position; it must learn to handle all positions.\n",
+ "\n",
+ "Since layer weights do not change, you might think of the sequential layers as the \"same layer\" repeated. In fact PyTorch makes this concrete; we can just create one layer, and use it multiple times."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Our language model in PyTorch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create the language model module that we described earlier:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel1(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden) \n",
+ " self.h_h = nn.Linear(n_hidden, n_hidden) \n",
+ " self.h_o = nn.Linear(n_hidden,vocab_sz)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " h = F.relu(self.h_h(self.i_h(x[:,0])))\n",
+ " h = h + self.i_h(x[:,1])\n",
+ " h = F.relu(self.h_h(h))\n",
+ " h = h + self.i_h(x[:,2])\n",
+ " h = F.relu(self.h_h(h))\n",
+ " return self.h_o(h)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you see, we have created three layers:\n",
+ "\n",
+ "- The embedding layer (`i_h` for *input* to *hidden*)\n",
+ "- The linear layer to create the activations for the next word (`h_h` for *hidden* to *hidden*)\n",
+ "- A final linear layer to predict the fourth word (`h_o` for *hidden* to *output*)\n",
+ "\n",
+ "This might be easier to represent in pictorial form. Let's define a simple pictorial representation of basic neural networks. Here's how we're going to represent a neural net with one hidden layer:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each shape represents activations: rectangle for input, circle for hidden (inner) layer activations, and triangle for output activations:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "An arrow represents the actual layer computation—i.e. the linear layer followed by the activation layers. Using this notation, here's what our simple language model looks like:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To simplify things, we've removed the details of the layer computation from each arrow. We've also color-coded the arrows, such that all arrows with the same color have the same weight matrix. For instance, all the input layers use the same embedding matrix, so they all have the same color (green).\n",
+ "\n",
+ "Let's try training this model and see how it goes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.824297 | \n", + "1.970941 | \n", + "0.467554 | \n", + "00:02 | \n", + "
| 1 | \n", + "1.386973 | \n", + "1.823242 | \n", + "0.467554 | \n", + "00:02 | \n", + "
| 2 | \n", + "1.417556 | \n", + "1.654497 | \n", + "0.494414 | \n", + "00:02 | \n", + "
| 3 | \n", + "1.376440 | \n", + "1.650849 | \n", + "0.494414 | \n", + "00:02 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.816274 | \n", + "1.964143 | \n", + "0.460185 | \n", + "00:02 | \n", + "
| 1 | \n", + "1.423805 | \n", + "1.739964 | \n", + "0.473259 | \n", + "00:02 | \n", + "
| 2 | \n", + "1.430327 | \n", + "1.685172 | \n", + "0.485382 | \n", + "00:02 | \n", + "
| 3 | \n", + "1.388390 | \n", + "1.657033 | \n", + "0.470406 | \n", + "00:02 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You will see that there is a set of activations which are being updated each time through the loop, and are stored in the variable `h` — this is called the *hidden state*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> Jargon: hidden state: the activations that are updated at each step of a recurrent neural network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A neural network which is defined using a loop like this is called a *recurrent neural network*, also known as an RNN. It is important to realise that an RNN is not a complicated new architecture, but is simply a refactoring of a multilayer neural network using a for loop.\n",
+ "\n",
+ "> A: My true opinion: if they were called \"looping neural networks\", or LNNs, they would seem 50% less daunting!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Improving the RNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Maintaining the state of an RNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the code for our RNN, one thing that seems problematic is that we are initialising our hidden state to zero for every new input sequence. Why is that a problem? We made our sample sequences short so they would fit easily into batches. But if we order those samples correctly, those sample sequences will be read in order by the model, exposing the model to long stretches of the original sequence. \n",
+ "\n",
+ "But because we initialize the model's hidden state to zero for each new sample, we are throwing away all the information we have about the sentences we have seen so far, which means that our model doesn't actually know where we are up to in the overall counting sequence. This is easily fixed; we can simply move the initialisation of the hidden state to `__init__`.\n",
+ "\n",
+ "But this fix will create its own subtle, but important, problem. It effectively makes our neural network as deep as the entire number of tokens in our document. For instance, if there were 10,000 tokens in our dataset, we would be creating a 10,000 layer neural network.\n",
+ "\n",
+ "To see this, consider the original pictorial representation of our recurrent neural network, before refactoring it with a for loop. You can see each layer corresponds with one token input. When we talk about the representation of a recurrent neural network before refactoring with the for loop, we call this the *unrolled representation*. It is often helpful to consider the unrolled representation when trying to understand an RNN.\n",
+ "\n",
+ "The problem with a 10,000 layer neural network is that if and when you get to the 10,000th word of the dataset, you will still need to calculate the derivatives all the way back to the first layer. This is going to be very slow indeed, and very memory intensive. It is unlikely that you could store even one mini batch on your GPU.\n",
+ "\n",
+ "The solution to this is to tell PyTorch that we do not want to back propagate the derivatives through the entire implicit neural network. Instead, we will just keep the last three layers of gradients. To remove all of the gradient history in PyTorch, we use the `detach` method.\n",
+ "\n",
+ "Here is the new version of our RNN. It is now stateful, because it remembers its activations between different calls to `forward`, which represent its use for different samples in the batch:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel3(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden) \n",
+ " self.h_h = nn.Linear(n_hidden, n_hidden) \n",
+ " self.h_o = nn.Linear(n_hidden,vocab_sz)\n",
+ " self.h = 0\n",
+ " \n",
+ " def forward(self, x):\n",
+ " for i in range(3):\n",
+ " self.h = self.h + self.i_h(x[:,i])\n",
+ " self.h = F.relu(self.h_h(self.h))\n",
+ " out = self.h_o(self.h)\n",
+ " self.h = self.h.detach()\n",
+ " return out\n",
+ " \n",
+ " def reset(self): self.h = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you think about it, this model will have the same activations whatever the sequence length we pick, because the hidden state will remember the last activation from the previous batch. The only thing that will be different are the gradients computed at each step: they will only be calculated on sequence length tokens in the past, instead of the whole stream. That is why this sequence length is often called *bptt* for back-propagation through time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* jargon: Back propagation through time (BPTT): Treating a neural net with effectively one layer per time step (usually refactored using a loop) as one big model, and calculating gradients on it in the usual way. To avoid running out of memory and time, we usually use _truncated_ BPTT, which \"detaches\" the history of computation steps in the hidden state every few time steps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To use `LMModel3`, we need to make sure the samples are going to be seen in a certain order. As we saw in the previous chapter, if the first line of the first batch is our `dset[0]` then the second batch should have `dset[1]` as the first line, so that the model sees the text flowing.\n",
+ "\n",
+ "`LMDataLoader` was doing this for us in the previous chapter. This time we're going to do it ourselves.\n",
+ "\n",
+ "To do this, we are going to rearrange our dataset. First we divide the samples into `m = len(dset) // bs` groups (this is the equivalent of splitting the whole concatenated dataset into, for instance, 64 equally sized pieces, since we're using `bs=64` here). `m` is the length of each of these pieces. For instance, if we're using our whole dataset (although we'll actually split it into train vs valid in a moment), that will be:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(328, 64, 21031)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = len(seqs)//bs\n",
+ "m,bs,len(seqs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first batch will be composed of the samples:\n",
+ "\n",
+ " (0, m, 2*m, ..., (bs-1)*m)\n",
+ "\n",
+ "then the second batch of the samples: \n",
+ "\n",
+ " (1, m+1, 2*m+1, ..., (bs-1)*m+1)\n",
+ "\n",
+ "and so forth. This way, at each epoch, the model will see a chunk of contiguous text of size `3*m` (since each text is of size 3) on each line of the batch.\n",
+ "\n",
+ "The following function does that reindexing:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def group_chunks(ds, bs):\n",
+ " m = len(ds) // bs\n",
+ " new_ds = L()\n",
+ " for i in range(m): new_ds += L(ds[i + m*j] for j in range(bs))\n",
+ " return new_ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we just pass `drop_last=True` when building our `DataLoaders` to drop the last batch that has not a shape of `bs`, we also pass `shuffle=False` to make sure the texts are read in order."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cut = int(len(seqs) * 0.8)\n",
+ "dls = DataLoaders.from_dsets(group_chunks(seqs[:cut], bs), group_chunks(seqs[cut:], bs), bs=bs, drop_last=True, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The last thing we add is a little tweak of the training loop via a `Callback`. We will talk more about callbacks in <| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1.677074 | \n", + "1.827367 | \n", + "0.467548 | \n", + "00:02 | \n", + "
| 1 | \n", + "1.282722 | \n", + "1.870913 | \n", + "0.388942 | \n", + "00:02 | \n", + "
| 2 | \n", + "1.090705 | \n", + "1.651793 | \n", + "0.462500 | \n", + "00:02 | \n", + "
| 3 | \n", + "1.005092 | \n", + "1.613794 | \n", + "0.516587 | \n", + "00:02 | \n", + "
| 4 | \n", + "0.965975 | \n", + "1.560775 | \n", + "0.551202 | \n", + "00:02 | \n", + "
| 5 | \n", + "0.916182 | \n", + "1.595857 | \n", + "0.560577 | \n", + "00:02 | \n", + "
| 6 | \n", + "0.897657 | \n", + "1.539733 | \n", + "0.574279 | \n", + "00:02 | \n", + "
| 7 | \n", + "0.836274 | \n", + "1.585141 | \n", + "0.583173 | \n", + "00:02 | \n", + "
| 8 | \n", + "0.805877 | \n", + "1.629808 | \n", + "0.586779 | \n", + "00:02 | \n", + "
| 9 | \n", + "0.795096 | \n", + "1.651267 | \n", + "0.588942 | \n", + "00:02 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is easy enough to add. We need to first change our data so that the dependent variable has each of the three next words after each of our three input words. Instead of 3, we use an attribute, `sl` (for sequence length) and make it a bit bigger:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sl = 16\n",
+ "seqs = L((tensor(nums[i:i+sl]), tensor(nums[i+1:i+sl+1]))\n",
+ " for i in range(0,len(nums)-sl-1,sl))\n",
+ "cut = int(len(seqs) * 0.8)\n",
+ "dls = DataLoaders.from_dsets(group_chunks(seqs[:cut], bs),\n",
+ " group_chunks(seqs[cut:], bs),\n",
+ " bs=bs, drop_last=True, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the first element of `seqs`, we can see that it contains two lists of the same size. The second list is the same as the first, but offset by one element:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(#16) ['one','.','two','.','three','.','four','.','five','.'...],\n",
+ " (#16) ['.','two','.','three','.','four','.','five','.','six'...]]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[L(vocab[o] for o in s) for s in seqs[0]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need to modify our model so that it outputs a prediction after every word, rather than just at the end of a three word sequence:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel4(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden) \n",
+ " self.h_h = nn.Linear(n_hidden, n_hidden) \n",
+ " self.h_o = nn.Linear(n_hidden,vocab_sz)\n",
+ " self.h = 0\n",
+ " \n",
+ " def forward(self, x):\n",
+ " outs = []\n",
+ " for i in range(sl):\n",
+ " self.h = self.h + self.i_h(x[:,i])\n",
+ " self.h = F.relu(self.h_h(self.h))\n",
+ " outs.append(self.h_o(self.h))\n",
+ " self.h = self.h.detach()\n",
+ " return torch.stack(outs, dim=1)\n",
+ " \n",
+ " def reset(self): self.h = 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model will return outputs of shape `bs x sl x vocab_sz` (since we stacked on `dim=1`). Our targets are of shape `bs x sl`, so we need to flatten those before using them in `F.cross_entropy`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def loss_func(inp, targ):\n",
+ " return F.cross_entropy(inp.view(-1, len(vocab)), targ.view(-1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now use this loss function to train the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "3.103298 | \n", + "2.874341 | \n", + "0.212565 | \n", + "00:01 | \n", + "
| 1 | \n", + "2.231964 | \n", + "1.971280 | \n", + "0.462158 | \n", + "00:01 | \n", + "
| 2 | \n", + "1.711358 | \n", + "1.813547 | \n", + "0.461182 | \n", + "00:01 | \n", + "
| 3 | \n", + "1.448516 | \n", + "1.828176 | \n", + "0.483236 | \n", + "00:01 | \n", + "
| 4 | \n", + "1.288630 | \n", + "1.659564 | \n", + "0.520671 | \n", + "00:01 | \n", + "
| 5 | \n", + "1.161470 | \n", + "1.714023 | \n", + "0.554932 | \n", + "00:01 | \n", + "
| 6 | \n", + "1.055568 | \n", + "1.660916 | \n", + "0.575033 | \n", + "00:01 | \n", + "
| 7 | \n", + "0.960765 | \n", + "1.719624 | \n", + "0.591064 | \n", + "00:01 | \n", + "
| 8 | \n", + "0.870153 | \n", + "1.839560 | \n", + "0.614665 | \n", + "00:01 | \n", + "
| 9 | \n", + "0.808545 | \n", + "1.770278 | \n", + "0.624349 | \n", + "00:01 | \n", + "
| 10 | \n", + "0.758084 | \n", + "1.842931 | \n", + "0.610758 | \n", + "00:01 | \n", + "
| 11 | \n", + "0.719320 | \n", + "1.799527 | \n", + "0.646566 | \n", + "00:01 | \n", + "
| 12 | \n", + "0.683439 | \n", + "1.917928 | \n", + "0.649821 | \n", + "00:01 | \n", + "
| 13 | \n", + "0.660283 | \n", + "1.874712 | \n", + "0.628581 | \n", + "00:01 | \n", + "
| 14 | \n", + "0.646154 | \n", + "1.877519 | \n", + "0.640055 | \n", + "00:01 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "…or in an unrolled representation:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's save some time by using PyTorch's RNN class, which implements exactly what we have created above, but also gives us the option to stack multiple RNNs, as we have discussed:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel5(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden, n_layers):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden)\n",
+ " self.rnn = nn.RNN(n_hidden, n_hidden, n_layers, batch_first=True)\n",
+ " self.h_o = nn.Linear(n_hidden, vocab_sz)\n",
+ " self.h = torch.zeros(n_layers, bs, n_hidden)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " res,h = self.rnn(self.i_h(x), self.h)\n",
+ " self.h = h.detach()\n",
+ " return self.h_o(res)\n",
+ " \n",
+ " def reset(self): self.h.zero_()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "3.055853 | \n", + "2.591640 | \n", + "0.437907 | \n", + "00:01 | \n", + "
| 1 | \n", + "2.162359 | \n", + "1.787310 | \n", + "0.471598 | \n", + "00:01 | \n", + "
| 2 | \n", + "1.710663 | \n", + "1.941807 | \n", + "0.321777 | \n", + "00:01 | \n", + "
| 3 | \n", + "1.520783 | \n", + "1.999726 | \n", + "0.312012 | \n", + "00:01 | \n", + "
| 4 | \n", + "1.330846 | \n", + "2.012902 | \n", + "0.413249 | \n", + "00:01 | \n", + "
| 5 | \n", + "1.163297 | \n", + "1.896192 | \n", + "0.450684 | \n", + "00:01 | \n", + "
| 6 | \n", + "1.033813 | \n", + "2.005209 | \n", + "0.434814 | \n", + "00:01 | \n", + "
| 7 | \n", + "0.919090 | \n", + "2.047083 | \n", + "0.456706 | \n", + "00:01 | \n", + "
| 8 | \n", + "0.822939 | \n", + "2.068031 | \n", + "0.468831 | \n", + "00:01 | \n", + "
| 9 | \n", + "0.750180 | \n", + "2.136064 | \n", + "0.475098 | \n", + "00:01 | \n", + "
| 10 | \n", + "0.695120 | \n", + "2.139140 | \n", + "0.485433 | \n", + "00:01 | \n", + "
| 11 | \n", + "0.655752 | \n", + "2.155081 | \n", + "0.493652 | \n", + "00:01 | \n", + "
| 12 | \n", + "0.629650 | \n", + "2.162583 | \n", + "0.498535 | \n", + "00:01 | \n", + "
| 13 | \n", + "0.613583 | \n", + "2.171649 | \n", + "0.491048 | \n", + "00:01 | \n", + "
| 14 | \n", + "0.604309 | \n", + "2.180355 | \n", + "0.487874 | \n", + "00:01 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This inaccuracy means that often the gradients calculated for updating the weights end up as zero or infinity for deep networks. This is commonly refered to as *vanishing gradients* or *exploding gradients*. That means that in SGD, the weights are updated either not at all, or jump to infinity. Either way, they won't improve with training.\n",
+ "\n",
+ "Researchers have developed a number of ways to tackle this problem, which we will be discussing later in the book. One way to tackle the problem is to change the definition of a layer in a way that makes it less likely to have exploding activations. We'll look at the details of how this is done in <"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this picture, our input $x_{t}$ enters on the bottom with the previous hidden state ($h_{t-1}$) and cell state ($x_{t-1}$). The four orange boxes represent four layers with the activation being either sigmoid (for $\\sigma$) or tanh. tanh is just a sigmoid rescaled to the range -1 to 1. Its mathematical expression can be written like this:\n",
+ "\n",
+ "$$\\tanh(x) = \\frac{e^{x} + e^{-x}}{e^{x}-e^{-x}} = 2 \\sigma(2x) - 1$$\n",
+ "\n",
+ "where $\\sigma$ is the sigmoid function. The green boxes are elementwise operations. What goes out is the new hidden state ($h_{t}$) and new cell state ($c_{t}$) on the left, ready for our next input. The new hidden state is also use as output, which is why the arrow splits to go up.\n",
+ "\n",
+ "Let's go over the four neural nets (called *gates*) one by one and explain the diagram, but before this, notice how very little the cell state (on the top) is changed. It doesn't even go directly through a neural net! This is exactly why it will carry on a longer-term state.\n",
+ "\n",
+ "First, the arrows for input and old hidden state are joined together. In the RNN we wrote in the past chapter, we were adding them together. In the LSTM, we stack them in one big tensor. This means the dimension of our embeddings (which is the dimension of $x_{t}$) can be different than the dimension of our hidden state. If we call those `n_in` and `n_hid`, the arrow at the bottom is of size `n_in + n_hid`, thus all the neural nets (orange boxes) are linear layers with `n_in + n_hid` inputs and `n_hid` outputs.\n",
+ "\n",
+ "The first gate (looking from the left to right) is called the *forget gate*. Since it's a linear layer followed by a sigmoid, its output will have scalars between 0 and 1. We multiply this result y the cell gate, so for all the values close to 0, we will forget what was inside that cell state (and for the values close to 1 it doesn't do anything). This gives the ability to the LSTM to forget things about its longterm state. For instance, when crossing a period or an `xxbos` token, we would expect to it to (have learned to) reset its cell state.\n",
+ "\n",
+ "The second gate works is called the *input gate*. It works with the third gate (which doesn't really have a name but is sometimes called the *cell gate*) to update the cell state. For instance we may see a new gender pronoum, so we must replace the information about gender that the forget gate removed by the new one. Like the forget gate, the input gate ends up on a product, so it jsut decides which element of the cell state to update (valeus close to 1) or not (values close to 0). The third gate will then fill those values with things between -1 and 1 (thanks to the tanh). The result is then added to the cell state.\n",
+ "\n",
+ "The last gate is the *output gate*. It will decides which information take in the cell state to generate the output. The cell state goes through a tanh before this and the output gate combined with the sigmoid decides which values to take inside it.\n",
+ "\n",
+ "In terms of code, we can write the same steps like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LSTMCell(Module):\n",
+ " def __init__(self, ni, nh):\n",
+ " self.forget_gate = nn.Linear(ni + nh, nh)\n",
+ " self.input_gate = nn.Linear(ni + nh, nh)\n",
+ " self.cell_gate = nn.Linear(ni + nh, nh)\n",
+ " self.output_gate = nn.Linear(ni + nh, nh)\n",
+ "\n",
+ " def forward(self, input, state):\n",
+ " h,c = state\n",
+ " h = torch.stack([h, input], dim=1)\n",
+ " forget = torch.sigmoid(self.forget_gate(h))\n",
+ " c = c * forget\n",
+ " inp = torch.sigmoid(self.input_gate(h))\n",
+ " cell = torch.tanh(self.cell_gate(h))\n",
+ " c = c + inp * cell\n",
+ " out = torch.sigmoid(self.output_gate(h))\n",
+ " h = outgate * torch.tanh(c)\n",
+ " return h, (h,c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In practice, we can then refactor the code. Also, in terms of performance, it's better to do one big matrix multiplication than four smaller ones (that's because we only launch the special fast kernel on GPU once, and it gives the GPU more work to do in parallel). The stacking takes a bit of time (since we have to move one of the tensors around on the GPU to have it all in a contiguous array), so we use two separate layers for the input and the hidden state. The optimized and refactored code then looks like that:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LSTMCell(Module):\n",
+ " def __init__(self, ni, nh):\n",
+ " self.ih = nn.Linear(ni,4*nh)\n",
+ " self.hh = nn.Linear(nh,4*nh)\n",
+ "\n",
+ " def forward(self, input, state):\n",
+ " h,c = state\n",
+ " #One big multiplication for all the gates is better than 4 smaller ones\n",
+ " gates = (self.ih(input) + self.hh(h)).chunk(4, 1)\n",
+ " ingate,forgetgate,outgate = map(torch.sigmoid, gates[:3])\n",
+ " cellgate = gates[3].tanh()\n",
+ "\n",
+ " c = (forgetgate*c) + (ingate*cellgate)\n",
+ " h = outgate * c.tanh()\n",
+ " return h, (h,c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we use the PyTorch `chunk` method to split our tensor into 4 pieces, e.g.:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t = torch.arange(0,10); t"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([0, 1, 2, 3, 4]), tensor([5, 6, 7, 8, 9]))"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t.chunk(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training a language model using LSTMs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the same network as before, using a two-layer LSTM. We can train it at a higher learning rate, for a shorter time, and get better accuracy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LMModel6(Module):\n",
+ " def __init__(self, vocab_sz, n_hidden, n_layers):\n",
+ " self.i_h = nn.Embedding(vocab_sz, n_hidden)\n",
+ " self.rnn = nn.LSTM(n_hidden, n_hidden, n_layers, batch_first=True)\n",
+ " self.h_o = nn.Linear(n_hidden, vocab_sz)\n",
+ " self.h = [torch.zeros(2, bs, n_hidden) for _ in range(n_layers)]\n",
+ " \n",
+ " def forward(self, x):\n",
+ " res,h = self.rnn(self.i_h(x), self.h)\n",
+ " self.h = [h_.detach() for h_ in h]\n",
+ " return self.h_o(res)\n",
+ " \n",
+ " def reset(self): \n",
+ " for h in self.h: h.zero_()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "3.000821 | \n", + "2.663942 | \n", + "0.438314 | \n", + "00:02 | \n", + "
| 1 | \n", + "2.139642 | \n", + "2.184780 | \n", + "0.240479 | \n", + "00:02 | \n", + "
| 2 | \n", + "1.607275 | \n", + "1.812682 | \n", + "0.439779 | \n", + "00:02 | \n", + "
| 3 | \n", + "1.347711 | \n", + "1.830982 | \n", + "0.497477 | \n", + "00:02 | \n", + "
| 4 | \n", + "1.123113 | \n", + "1.937766 | \n", + "0.594401 | \n", + "00:02 | \n", + "
| 5 | \n", + "0.852042 | \n", + "2.012127 | \n", + "0.631592 | \n", + "00:02 | \n", + "
| 6 | \n", + "0.565494 | \n", + "1.312742 | \n", + "0.725749 | \n", + "00:02 | \n", + "
| 7 | \n", + "0.347445 | \n", + "1.297934 | \n", + "0.711263 | \n", + "00:02 | \n", + "
| 8 | \n", + "0.208191 | \n", + "1.441269 | \n", + "0.731201 | \n", + "00:02 | \n", + "
| 9 | \n", + "0.126335 | \n", + "1.569952 | \n", + "0.737305 | \n", + "00:02 | \n", + "
| 10 | \n", + "0.079761 | \n", + "1.427187 | \n", + "0.754150 | \n", + "00:02 | \n", + "
| 11 | \n", + "0.052990 | \n", + "1.494990 | \n", + "0.745117 | \n", + "00:02 | \n", + "
| 12 | \n", + "0.039008 | \n", + "1.393731 | \n", + "0.757894 | \n", + "00:02 | \n", + "
| 13 | \n", + "0.031502 | \n", + "1.373210 | \n", + "0.758464 | \n", + "00:02 | \n", + "
| 14 | \n", + "0.028068 | \n", + "1.368083 | \n", + "0.758464 | \n", + "00:02 | \n", + "
\n",
+ "\n",
+ "Hinton used a nice metaphor when he explained, in an interview, the inspiration for dropout:\n",
+ "\n",
+ "> : \"I went to my bank. The tellers kept changing and I asked one of them why. He said he didn’t know but they got moved around a lot. I figured it must be because it would require cooperation between employees to successfully defraud the bank. This made me realize that randomly removing a different subset of neurons on each example would prevent conspiracies and thus reduce overfitting\"\n",
+ "\n",
+ "In the same interview, he also explained that neuroscience provided additional inspiration:\n",
+ "\n",
+ "> : \"We don't really know why neurons spike. One theory is that they want to be noisy so as to regularize, because we have many more parameters than we have data points. The idea of dropout is that if you have noisy activations, you can afford to use a much bigger model.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see there that if we just zero those activations without doing anything else, our model will have problems to train: if we go from the sum of 5 activations (that are all positive numbers since we apply a ReLU) to just 2, this won't have the same scale. Therefore if we dropout with a probability `p`, we rescale all activation by dividing them by `1-p` (on average `p` will be zeroed, so it leaves `1-p`), as shown in this diagram from the original paper:\n",
+ "\n",
+ "\n",
+ "\n",
+ "This is a full implementation of the dropout layer in PyTorch (although PyTorch's native layer is actually written in C, not Python):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Dropout(Module):\n",
+ " def __init__(self, p): self.p = p\n",
+ " def forward(self, x):\n",
+ " if not self.training: return x\n",
+ " mask = x.new(*x.shape).bernoulli_(1-p)\n",
+ " return x * mask.div_(1-p)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `bernoulli_` method is creating a tensor with random zeros (with probability p) and ones (with probability 1-p), which is then multiplied with our input before dividing by `1-p`. Note the use of the `training` attribute, which is available in any PyTorch `nn.Module`, and tells us if we are doing training or inference.\n",
+ "\n",
+ "> note: In previous chapters of the book we'd be adding a code example for `bernoulli_` here, so you can see exactly how it works. But now that you know enough to do this yourself, we're going to be doing fewer and fewer examples for you, and instead expecting you to do your own experiments to see how things work. In this case, you'll see in the end-of-chapter questionnaire that we're asking you to experiment with `bernoulli_`--but don't wait for us to ask you to experiment to develop your understanding of the code we're studying, go ahead and do it anyway!\n",
+ "\n",
+ "Using dropout before passing the output of our LSTM to the final layer will help reduce overfitting. Dropout is also used in many other models, including the default CNN head used in `fastai.vision`, and is also available in `fastai.tabular` by passing the `ps` parameter (where each \"p\" is passed to each added `Dropout` layer), as we'll see in <| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2.693885 | \n", + "2.013484 | \n", + "0.466634 | \n", + "00:02 | \n", + "
| 1 | \n", + "1.685549 | \n", + "1.187310 | \n", + "0.629313 | \n", + "00:02 | \n", + "
| 2 | \n", + "0.973307 | \n", + "0.791398 | \n", + "0.745605 | \n", + "00:02 | \n", + "
| 3 | \n", + "0.555823 | \n", + "0.640412 | \n", + "0.794108 | \n", + "00:02 | \n", + "
| 4 | \n", + "0.351802 | \n", + "0.557247 | \n", + "0.836100 | \n", + "00:02 | \n", + "
| 5 | \n", + "0.244986 | \n", + "0.594977 | \n", + "0.807292 | \n", + "00:02 | \n", + "
| 6 | \n", + "0.192231 | \n", + "0.511690 | \n", + "0.846761 | \n", + "00:02 | \n", + "
| 7 | \n", + "0.162456 | \n", + "0.520370 | \n", + "0.858073 | \n", + "00:02 | \n", + "
| 8 | \n", + "0.142664 | \n", + "0.525918 | \n", + "0.842285 | \n", + "00:02 | \n", + "
| 9 | \n", + "0.128493 | \n", + "0.495029 | \n", + "0.858073 | \n", + "00:02 | \n", + "
| 10 | \n", + "0.117589 | \n", + "0.464236 | \n", + "0.867188 | \n", + "00:02 | \n", + "
| 11 | \n", + "0.109808 | \n", + "0.466550 | \n", + "0.869303 | \n", + "00:02 | \n", + "
| 12 | \n", + "0.104216 | \n", + "0.455151 | \n", + "0.871826 | \n", + "00:02 | \n", + "
| 13 | \n", + "0.100271 | \n", + "0.452659 | \n", + "0.873617 | \n", + "00:02 | \n", + "
| 14 | \n", + "0.098121 | \n", + "0.458372 | \n", + "0.869385 | \n", + "00:02 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The grey grid to the left is our *image* we're going to apply the kernel to. The convolution operation multiplies each element of the kernel, to each element of a 3x3 block of the image. The results of these multiplications are then added together. The diagram above shows an example of applying a kernel to a single location in the image, the 3x3 block around cell 18.\n",
+ "\n",
+ "Let's do this with code. First, we create a little 3x3 matrix like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "top_edge = tensor([[-1,-1,-1],\n",
+ " [ 0, 0, 0],\n",
+ " [ 1, 1, 1]]).float()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We're going to call this our *kernel*\n",
+ "(because that's what fancy computer vision researchers call these). And we'll need an image, of course:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "im3 = Image.open(path/'train'/'3'/'12.png')\n",
+ "show_image(im3);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we're going to take the top 3x3 pixel square of our image, and we'll multiply each of those by each item in our kernel. Then we'll add them up. Like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[-0., -0., -0.],\n",
+ " [0., 0., 0.],\n",
+ " [0., 0., 0.]])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im3_t = tensor(im3)\n",
+ "im3_t[0:3,0:3] * top_edge"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(im3_t[0:3,0:3] * top_edge).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Not very interesting so far - they are all white pixels in the top left corner. But let's pick a couple of more interesting spots:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#hide_output\n",
+ "df = pd.DataFrame(im3_t[:10,:20])\n",
+ "df.style.set_properties(**{'font-size':'6pt'}).background_gradient('Greys')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Top](\"images/att_00059.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There's a top edge at cell 5,7. Let's repeat our calculation there:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(762.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(im3_t[4:7,6:9] * top_edge).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There's a right edge at cell 8,18. What does that give us?:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(-29.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(im3_t[7:10,17:20] * top_edge).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, this little calculation is returning a high number where the 3x3 pixel square represents a top edge (i.e. where there are low values at the top of the square, and high values immediately underneath). That's because the `-1` values in our kernel have little impact in that case, but the `1` values have a lot.\n",
+ "\n",
+ "Let's look a tiny bit at the math. The filter will take any window of size 3 by 3 in our images, and if we name the pixel values like this:\n",
+ "\n",
+ "$$\\begin{matrix} a1 & a2 & a3 \\\\ a4 & a5 & a6 \\\\ a7 & a8 & a9 \\end{matrix}$$\n",
+ "\n",
+ "it will return $a1+a2+a3-a7-a8-a9$. Now if we are in a part of the image where there $a1$, $a2$ and $a3$ are kind of the same as $a7$, $a8$ and $a9$, then the terms will cancel each other and we will get 0. However if $a1$ is greater than $a7$, $a2$ is greater than $a8$ and $a3$ is greater than $a9$, we will get a bigger number as a result. So this filter detects horizontal edges, more precisely edges where we go from bright parts of the image at the top to darker parts at the bottom.\n",
+ "\n",
+ "Changing our filter to have the row of ones at the top and the -1 at the bottom would detect horizonal edges that go from dark to light. Putting the ones and -1 in columns versus rows would give us a filter that detect vertical edges. Each set of weights will produce a different kind of outcome.\n",
+ "\n",
+ "Let's create a function to do this for one location, and check it matches our result from before:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def apply_kernel(row, col, kernel):\n",
+ " return (im3_t[row-1:row+2,col-1:col+2] * kernel).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(762.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "apply_kernel(5,7,top_edge)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But note that we can't apply it to the corner (such as location 0,0), since there isn't a complete 3x3 square there."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Mapping a convolution kernel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can map `apply_kernel()` across the coordinate grid. That is, we'll be taking our 3x3 kernel, and applying it to each 3x3 section of our image. For instance, here are the positions a 3x3 kernel can be applied to in the first row of a 5x5 image:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Applying](\"images/chapter9_nopadconv.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get a *grid* of coordinates we can use a *nested list comprehension*, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[[(1, 1), (1, 2), (1, 3), (1, 4)],\n",
+ " [(2, 1), (2, 2), (2, 3), (2, 4)],\n",
+ " [(3, 1), (3, 2), (3, 3), (3, 4)],\n",
+ " [(4, 1), (4, 2), (4, 3), (4, 4)]]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[[(i,j) for j in range(1,5)] for i in range(1,5)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: Nested list comprehensions are used a lot in Python, so if you haven't seen them before, take a few minutes to make sure you understand what's happening here, and experiment with writing your own nested list comprehensions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's the result of applying our kernel over a coordinate grid."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 4 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "12 | \n", + "99 | \n", + "91 | \n", + "142 | \n", + "155 | \n", + "246 | \n", + "182 | \n", + "155 | \n", + "155 | \n", + "155 | \n", + "155 | \n", + "131 | \n", + "52 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "
| 6 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "138 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "252 | \n", + "210 | \n", + "122 | \n", + "33 | \n", + "0 | \n", + "
| 7 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "220 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "235 | \n", + "189 | \n", + "189 | \n", + "189 | \n", + "189 | \n", + "150 | \n", + "189 | \n", + "205 | \n", + "254 | \n", + "254 | \n", + "254 | \n", + "75 | \n", + "0 | \n", + "
| 8 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "35 | \n", + "74 | \n", + "35 | \n", + "35 | \n", + "25 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "13 | \n", + "224 | \n", + "254 | \n", + "254 | \n", + "153 | \n", + "0 | \n", + "
| 9 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "90 | \n", + "254 | \n", + "254 | \n", + "247 | \n", + "53 | \n", + "0 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There's a top edge at cell 5,7. Let's repeat our calculation there:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(762.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(im3_t[4:7,6:9] * top_edge).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There's a right edge at cell 8,18. What does that give us?:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(-29.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(im3_t[7:10,17:20] * top_edge).sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, this little calculation is returning a high number where the 3x3 pixel square represents a top edge (i.e. where there are low values at the top of the square, and high values immediately underneath). That's because the `-1` values in our kernel have little impact in that case, but the `1` values have a lot.\n",
+ "\n",
+ "Let's look a tiny bit at the math. The filter will take any window of size 3 by 3 in our images, and if we name the pixel values like this:\n",
+ "\n",
+ "$$\\begin{matrix} a1 & a2 & a3 \\\\ a4 & a5 & a6 \\\\ a7 & a8 & a9 \\end{matrix}$$\n",
+ "\n",
+ "it will return $a1+a2+a3-a7-a8-a9$. Now if we are in a part of the image where there $a1$, $a2$ and $a3$ are kind of the same as $a7$, $a8$ and $a9$, then the terms will cancel each other and we will get 0. However if $a1$ is greater than $a7$, $a2$ is greater than $a8$ and $a3$ is greater than $a9$, we will get a bigger number as a result. So this filter detects horizontal edges, more precisely edges where we go from bright parts of the image at the top to darker parts at the bottom.\n",
+ "\n",
+ "Changing our filter to have the row of ones at the top and the -1 at the bottom would detect horizonal edges that go from dark to light. Putting the ones and -1 in columns versus rows would give us a filter that detect vertical edges. Each set of weights will produce a different kind of outcome.\n",
+ "\n",
+ "Let's create a function to do this for one location, and check it matches our result from before:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def apply_kernel(row, col, kernel):\n",
+ " return (im3_t[row-1:row+2,col-1:col+2] * kernel).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(762.)"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "apply_kernel(5,7,top_edge)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But note that we can't apply it to the corner (such as location 0,0), since there isn't a complete 3x3 square there."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Mapping a convolution kernel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can map `apply_kernel()` across the coordinate grid. That is, we'll be taking our 3x3 kernel, and applying it to each 3x3 section of our image. For instance, here are the positions a 3x3 kernel can be applied to in the first row of a 5x5 image:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get a *grid* of coordinates we can use a *nested list comprehension*, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[[(1, 1), (1, 2), (1, 3), (1, 4)],\n",
+ " [(2, 1), (2, 2), (2, 3), (2, 4)],\n",
+ " [(3, 1), (3, 2), (3, 3), (3, 4)],\n",
+ " [(4, 1), (4, 2), (4, 3), (4, 4)]]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[[(i,j) for j in range(1,5)] for i in range(1,5)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> note: Nested list comprehensions are used a lot in Python, so if you haven't seen them before, take a few minutes to make sure you understand what's happening here, and experiment with writing your own nested list comprehensions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's the result of applying our kernel over a coordinate grid."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rng = range(1,27)\n",
+ "top_edge3 = tensor([[apply_kernel(i,j,top_edge) for j in rng] for i in rng])\n",
+ "\n",
+ "show_image(top_edge3);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking good! Our top edges are black, and bottom edges are white (since they are the *opposite* of top edges). Now that our *image* contains negative numbers too, matplotlib has automatically changed our colors, so that white is the smallest number in the image, black the highest, and zeros appear as grey.\n",
+ "\n",
+ "We can try the same thing for left edges:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "left_edge = tensor([[-1,1,0],\n",
+ " [-1,1,0],\n",
+ " [-1,1,0]]).float()\n",
+ "\n",
+ "left_edge3 = tensor([[apply_kernel(i,j,left_edge) for j in rng] for i in rng])\n",
+ "\n",
+ "show_image(left_edge3);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This operation of applying a kernel over a grid in this way is called *convolution*. In the paper [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285) there are many great diagrams showing how image kernels can be applied. Here's an example from the paper showing (at bottom) a light blue 4x4 image, with a dark blue 3x3 kernel being applied, creating a 2x2 green output activation map at the top. (We'll be using quite a few images from this paper in this book--when you see images in this style, you'll know they're from this great paper.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Result](\"images/att_00028.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Look at the shape of the result. If the original image has a height of `h` and a width of `w`, how many 3 by 3 windows can we find? As you see from the example, there are `h-2` by `w-2` windows, so the image we get as a result as a height of `h-2` and a witdh of `w-2`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Convolutions in PyTorch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Convolution is such an important and widely-used operation that PyTorch has it builtin. It's called `F.conv2d`. The PyTorch docs tell us that it includes these parameters:\n",
+ "\n",
+ "- **input**: input tensor of shape `(minibatch, in_channels, iH, iW)`\n",
+ "- **weight**: filters of shape `(out_channels, in_channels, kH, kW)`\n",
+ "\n",
+ "Here `iH,iW` is the height and width of the image (i.e. `28,28`), and `kH,kW` is the height and width of our kernel (`3,3`). But apparently PyTorch is expecting rank 4 tensors for both these arguments, but currently we only have rank 2 tensors (i.e. matrices, arrays with two axes).\n",
+ "\n",
+ "The reason for these extra axes is that PyTorch has a few tricks up its sleeve. The first trick is that PyTorch can apply a convolution to multiple images at the same time. That means we can call it on every item in a batch at once!\n",
+ "\n",
+ "The second trick is that PyTorch can apply multiple kernels at the same time. So let's create the diagnoal edge kernels too, and then stack all 4 of our edge kernels into a single tensor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([4, 3, 3])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "diag1_edge = tensor([[ 0,-1, 1],\n",
+ " [-1, 1, 0],\n",
+ " [ 1, 0, 0]]).float()\n",
+ "diag2_edge = tensor([[ 1,-1, 0],\n",
+ " [ 0, 1,-1],\n",
+ " [ 0, 0, 1]]).float()\n",
+ "\n",
+ "edge_kernels = torch.stack([left_edge, top_edge, diag1_edge, diag2_edge])\n",
+ "edge_kernels.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to test on a mini-batch, we'll need a `DataLoader`, and a sample mini-batch. Let's use the data block API:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 1, 28, 28])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mnist = DataBlock((ImageBlock(cls=PILImageBW), CategoryBlock), \n",
+ " get_items=get_image_files, \n",
+ " splitter=GrandparentSplitter(),\n",
+ " get_y=parent_label)\n",
+ "\n",
+ "dls = mnist.dataloaders(path)\n",
+ "xb,yb = first(dls.valid)\n",
+ "xb.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By default, fastai puts data on the GPU when using data blocks. Let's move it to the CPU for our examples:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xb,yb = to_cpu(xb),to_cpu(yb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One batch contains 64 images, each of 1 channel, with 28x28 pixels. `F.conv2d` can handle multi-channel (e.g. colour) images. A *channel* is a single basic color in an image--for regular full color images there are 3 channels, red, green, and blue. PyTorch represents an image as a rank-3 tensor, with dimensions channels x rows x columns.\n",
+ "\n",
+ "We'll see how to handle more than one channel later in this chapter. Kernels passed to `F.conv2d` need to be rank-4 tensors: channels_in x features_out x rows x columns. `edge_kernels` is currently missing one of these: the `1` for features_out. We need to tell PyTorch that the number of input channels in the kernel is one, by inserting an axis of size one (this is known as a *unit axis*) in the first location, since the PyTorch docs show that's where `in_channels` is expected. To insert a unit axis into a tensor, use the `unsqueeze` method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([4, 3, 3]), torch.Size([4, 1, 3, 3]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "edge_kernels.shape,edge_kernels.unsqueeze(1).shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is now the correct shape for `edge_kernels`. Let's pass this all to `conv2d`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "edge_kernels = edge_kernels.unsqueeze(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 4, 26, 26])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch_features = F.conv2d(xb, edge_kernels)\n",
+ "batch_features.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The output shape shows our 64 images in the mini-batch, 4 kernels, and 26x26 edge maps (we started with 28x28 images, but lost one pixel from each side as discussed earlier). We can see we get the same results as when we did this manually:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Look at the shape of the result. If the original image has a height of `h` and a width of `w`, how many 3 by 3 windows can we find? As you see from the example, there are `h-2` by `w-2` windows, so the image we get as a result as a height of `h-2` and a witdh of `w-2`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Convolutions in PyTorch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Convolution is such an important and widely-used operation that PyTorch has it builtin. It's called `F.conv2d`. The PyTorch docs tell us that it includes these parameters:\n",
+ "\n",
+ "- **input**: input tensor of shape `(minibatch, in_channels, iH, iW)`\n",
+ "- **weight**: filters of shape `(out_channels, in_channels, kH, kW)`\n",
+ "\n",
+ "Here `iH,iW` is the height and width of the image (i.e. `28,28`), and `kH,kW` is the height and width of our kernel (`3,3`). But apparently PyTorch is expecting rank 4 tensors for both these arguments, but currently we only have rank 2 tensors (i.e. matrices, arrays with two axes).\n",
+ "\n",
+ "The reason for these extra axes is that PyTorch has a few tricks up its sleeve. The first trick is that PyTorch can apply a convolution to multiple images at the same time. That means we can call it on every item in a batch at once!\n",
+ "\n",
+ "The second trick is that PyTorch can apply multiple kernels at the same time. So let's create the diagnoal edge kernels too, and then stack all 4 of our edge kernels into a single tensor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([4, 3, 3])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "diag1_edge = tensor([[ 0,-1, 1],\n",
+ " [-1, 1, 0],\n",
+ " [ 1, 0, 0]]).float()\n",
+ "diag2_edge = tensor([[ 1,-1, 0],\n",
+ " [ 0, 1,-1],\n",
+ " [ 0, 0, 1]]).float()\n",
+ "\n",
+ "edge_kernels = torch.stack([left_edge, top_edge, diag1_edge, diag2_edge])\n",
+ "edge_kernels.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to test on a mini-batch, we'll need a `DataLoader`, and a sample mini-batch. Let's use the data block API:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 1, 28, 28])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mnist = DataBlock((ImageBlock(cls=PILImageBW), CategoryBlock), \n",
+ " get_items=get_image_files, \n",
+ " splitter=GrandparentSplitter(),\n",
+ " get_y=parent_label)\n",
+ "\n",
+ "dls = mnist.dataloaders(path)\n",
+ "xb,yb = first(dls.valid)\n",
+ "xb.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By default, fastai puts data on the GPU when using data blocks. Let's move it to the CPU for our examples:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xb,yb = to_cpu(xb),to_cpu(yb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One batch contains 64 images, each of 1 channel, with 28x28 pixels. `F.conv2d` can handle multi-channel (e.g. colour) images. A *channel* is a single basic color in an image--for regular full color images there are 3 channels, red, green, and blue. PyTorch represents an image as a rank-3 tensor, with dimensions channels x rows x columns.\n",
+ "\n",
+ "We'll see how to handle more than one channel later in this chapter. Kernels passed to `F.conv2d` need to be rank-4 tensors: channels_in x features_out x rows x columns. `edge_kernels` is currently missing one of these: the `1` for features_out. We need to tell PyTorch that the number of input channels in the kernel is one, by inserting an axis of size one (this is known as a *unit axis*) in the first location, since the PyTorch docs show that's where `in_channels` is expected. To insert a unit axis into a tensor, use the `unsqueeze` method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([4, 3, 3]), torch.Size([4, 1, 3, 3]))"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "edge_kernels.shape,edge_kernels.unsqueeze(1).shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is now the correct shape for `edge_kernels`. Let's pass this all to `conv2d`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "edge_kernels = edge_kernels.unsqueeze(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 4, 26, 26])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch_features = F.conv2d(xb, edge_kernels)\n",
+ "batch_features.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The output shape shows our 64 images in the mini-batch, 4 kernels, and 26x26 edge maps (we started with 28x28 images, but lost one pixel from each side as discussed earlier). We can see we get the same results as when we did this manually:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_image(batch_features[0,0]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The most important trick that PyTorch has up its sleeve is that it can use the GPU to do all this work in parallel. That is, applying multiple kernels, to multiple images, across multiple channels. Doing lots of work in parallel is critical to getting GPUs to work efficiently; if we did each of these one at a time, we'll often run hundreds of times slower (and if we used our manual convolution loop from the previous section, we'd be millions of times slower!) Therefore, to become a strong deep learning practitioner, one skill to practice is giving your GPU plenty of work to do at a time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Strides and padding"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It would be nice to not lose those two pixels on each axis. The way we do that is to add *padding*, which is simply additional pixels added around the outside of our image. Most commonly, pixels of zeros are added. With appropriate padding, we can ensure that the output activation map is the same size as the original image, which can make things a lot simpler when we construct our architectures."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Padding](\"images/chapter9_padconv.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a 5x5 input, and 4x4 kernel, and 2 pixels of padding, we end up with a 6x6 activation map:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"4x4](\"images/att_00029.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we add a kernel of size `ks` by `ks` (with `ks` an odd number), the necessary padding on each side to keep the same shape is `ks//2`. An even number for `ks` would require a different amount of padding on the top/bottom, left/right, but in practice we almost never use an even filter size.\n",
+ "\n",
+ "So far, when we have applied the kernel to the grid, we have moved it one pixel over at a time. But we can jump further; for instance, we could move over two pixels after each kernel application. This is known as a *stride 2* convolution. The most common kernel size in practice is 3x3, and the most common padding is 1. As you'll see, stride 2 convolutions are useful for decreasing the size of our outputs, and stride 1 convolutions are useful for adding layers without changing the output size."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"3x3](\"images/att_00030.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In an image of size `h` by `w` like before, using a padding of 1 and a stride of 2 will give us a result of size `(h+1)//2` by `(w+1)//2`. The general formula for each dimension is `(n + 2*pad - ks)//stride + 1` where `pad` is the padding, `ks` the size of our kernel and `stride` the stride."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### CNNs from different viewpoints"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "fast.ai student Matt Kleinsmith came up with the very clever idea of showing [CNNs from different viewpoints](https://medium.com/impactai/cnns-from-different-viewpoints-fab7f52d159c). In fact, it's so clever, and so helpful, we're going to show it here too!\n",
+ "\n",
+ "Here's our 3x3 pixel *image*, with each *pixel* labeled with a letter:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"The](\"images/att_00032.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and our kernel, with each weight labeled with a greek letter:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"The](\"images/att_00033.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since the filter fits in the image four times, we have four results:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"The](\"images/att_00034.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here’s how we applied the kernel to each section of the image to yield each result:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Applying](\"images/att_00035.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The equation view:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"The](\"images/att_00036.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that the bias term, b, is the same for each section of the image. You can consider the bias as part of the filter, just like the weights (α, β, γ, δ) are part of the filter.\n",
+ "\n",
+ "The compact equation view:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"The](\"images/att_00037.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's an interesting insight -- a convolution can be represented as a special kind of matrix multiplication. The weight matrix is just like the ones from traditional neural networks. However, this weight matrix has two special properties:\n",
+ "\n",
+ "1. The zeros shown in gray are untrainable. This means that they’ll stay zero throughout the optimization process.\n",
+ "1. Some of the weights are equal, and while they are trainable (i.e. changeable), they must remain equal. These are called *shared weights*.\n",
+ "\n",
+ "The zeros correspond to the pixels that the filter can't touch. Each row of the weight matrix corresponds to one application of the filter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Convolution](\"images/att_00038.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Our first convolutional neural network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Learning kernels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There is no reason to believe that these particular edge filters are the most useful kernels for image recognition. Furthermore, we've seen that in later layers convolutional kernels become complex transformations of features from lower levels — we do not have a good idea of how to manually construct these.\n",
+ "\n",
+ "Instead, it would be best to learn the values of the kernels. We already know how to do this — SGD! In effect, the model will learn the features that are useful for classification.\n",
+ "\n",
+ "When we use convolutions instead of (or in addition to) regular linear layers we create a *convolutional neural network*, or *CNN*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating the CNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's the basic neural network we had in <>:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simple_net = nn.Sequential(\n",
+ " nn.Linear(28*28,30),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(30,1)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can view a model's definition:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Sequential(\n",
+ " (0): Linear(in_features=784, out_features=30, bias=True)\n",
+ " (1): ReLU()\n",
+ " (2): Linear(in_features=30, out_features=1, bias=True)\n",
+ ")"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "simple_net"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now want to create a similar architecture to this linear model, but using convolutional layers instead of linear. `nn.Conv2d` is the module equivalent of `F.conv2d`. It's more convenient than `F.conv2d` when creating an architecture, because it creates the weight matrix for us automatically when we instantiate it.\n",
+ "\n",
+ "Here's a possible architecture:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "broken_cnn = sequential(\n",
+ " nn.Conv2d(1,30, kernel_size=3, padding=1),\n",
+ " nn.ReLU(),\n",
+ " nn.Conv2d(30,1, kernel_size=3, padding=1)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing to note here is that we didn't need to specify \"28x28\" as the input size. That's because a linear layer needs a weight in the weight matrix for every pixel. So it needs to know how many pixels there are. But a convolution is applied over each pixel automatically. The weights only depend on the number of input and output channels, and the kernel size, as we say in the previous section.\n",
+ "\n",
+ "Have a think about what the output shape is going to be.\n",
+ "\n",
+ "Let's try it and see:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 1, 28, 28])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "broken_cnn(xb).shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is not something we can use to do classification, since we need a single output activation per image, not a 28x28 map of activations. One way to deal with this is to use enough stride-2 convolutions such that the final layer is size 1. That is, after one stride-2 convolution, the size will be 14x14, after 2 it will be 7x7, then 4x4, 2x2, and finally size 1.\n",
+ "\n",
+ "Let's try that now. First, we'll define a function with the basic parameters we'll use in each convolution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def conv(ni, nf, ks=3, act=True):\n",
+ " res = nn.Conv2d(ni, nf, stride=2, kernel_size=ks, padding=ks//2)\n",
+ " if act: res = nn.Sequential(res, nn.ReLU())\n",
+ " return res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> important: Refactoring parts of your neural networks like this makes it much less likely you'll get errors due to inconsistencies in your architectures, and makes it more obvious to the reader which parts of your layers are actually changing."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we use a stride-2 convolution, we often increase the number of features at the same time. This is because we're decreasing the number of activations in the activation map by a factor of 4; we don't want to decrease the capacity of a layer by too much at a time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> jargon: channels and features: These two terms are largely used interchangably, and refer to the size of the second axis of a weight matrix, which is, therefore, the number of activations per grid cell after a convolution. *Features* is never used to refer to the input data, but *channels* can refer to either the input data (generally channels are colors) or activations inside the network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simple_cnn = sequential(\n",
+ " conv(1 ,4), #14x14\n",
+ " conv(4 ,8), #7x7\n",
+ " conv(8 ,16), #4x4\n",
+ " conv(16,32), #2x2\n",
+ " conv(32,2, act=False), #1x1\n",
+ " Flatten(),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> j: I like to add comments like the above after each convolution to show how large the activation map will be after each layer. The above comments assume that the input size is 28x28"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now the network outputs two activations, which maps to the two possible levels in our labels:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([64, 2])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "simple_cnn(xb).shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now create our `Learner`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = Learner(dls, simple_cnn, loss_func=F.cross_entropy, metrics=accuracy)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see exactly what's going on in your model, use `summary`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Sequential (Input shape: ['64 x 1 x 28 x 28'])\n",
+ "================================================================\n",
+ "Layer (type) Output Shape Param # Trainable \n",
+ "================================================================\n",
+ "Conv2d 64 x 4 x 14 x 14 40 True \n",
+ "________________________________________________________________\n",
+ "ReLU 64 x 4 x 14 x 14 0 False \n",
+ "________________________________________________________________\n",
+ "Conv2d 64 x 8 x 7 x 7 296 True \n",
+ "________________________________________________________________\n",
+ "ReLU 64 x 8 x 7 x 7 0 False \n",
+ "________________________________________________________________\n",
+ "Conv2d 64 x 16 x 4 x 4 1,168 True \n",
+ "________________________________________________________________\n",
+ "ReLU 64 x 16 x 4 x 4 0 False \n",
+ "________________________________________________________________\n",
+ "Conv2d 64 x 32 x 2 x 2 4,640 True \n",
+ "________________________________________________________________\n",
+ "ReLU 64 x 32 x 2 x 2 0 False \n",
+ "________________________________________________________________\n",
+ "Conv2d 64 x 2 x 1 x 1 578 True \n",
+ "________________________________________________________________\n",
+ "Flatten 64 x 2 0 False \n",
+ "________________________________________________________________\n",
+ "\n",
+ "Total params: 6,722\n",
+ "Total trainable params: 6,722\n",
+ "Total non-trainable params: 0\n",
+ "\n",
+ "Optimizer used: \n",
+ "Loss function: \n",
+ "\n",
+ "Callbacks:\n",
+ " - TrainEvalCallback\n",
+ " - Recorder\n",
+ " - ProgressCallback"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "learn.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the output of the final Conv2d layer is `64x2x1x1`. We need to remove those extra `1x1` axes; that's what `Flatten` does. It's basically the same as PyTorch's `squeeze` method, but as a module.\n",
+ "\n",
+ "Let's see if this trains! Since this is a deeper network than we've built from scratch before, we'll use a lower learning rate and more epochs:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(2, 0.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Success! It's getting closer to the resnet-18 result we had, although it's not quite there yet, and it's taking more epochs, and we're needing to use a lower learning rate. So we've got a few more tricks still to learn--but we're getting closer and closer to being able to create a modern CNN from scratch."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Understanding convolution arithmetic"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see from the summary that we have an input of size `64x1x28x28`. The axes are: `batch,channel,height,width`. This is often represented as `NCHW` (where `N` refers to batch size). Tensorflow, on the other hand, uses `NHWC` axis order. The first layer is:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Sequential(\n",
+ " (0): Conv2d(1, 4, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+ " (1): ReLU()\n",
+ ")"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = learn.model[0]\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So we have 1 channel input, 4 channel output, and a 3x3 kernel. Let's check the weights of the first convolution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([4, 1, 3, 3])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m[0].weight.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The summary shows we have 40 parameters, and `4*1*3*3` is 36. What are the other 4 parameters? Let's see what the bias contains:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([4])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m[0].bias.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now use this information to better understand our earlier statement in this section: \"because we're decreasing the number of activations in the activation map by a factor of 4; we don't want to decrease the capacity of a layer by too much at a time\".\n",
+ "\n",
+ "There is one bias for each channel. (Sometimes channels are called *features* or *filters* when they are not input channels.) The output shape is `64x4x14x14`, and this will therefore become the input shape to the next layer. The next layer, according to `summary`, has 296 parameters. Let's ignore the batch axis to keep things simple. So for each of `14*14=196` locations we are multiplying `296-8=288` weights (ignoring the bias for simplicity), so that's `196*288=56_448` multiplications at this layer. The next layer will have `7*7*(1168-16)=56_448` multiplications.\n",
+ "\n",
+ "So what happened here is that our stride 2 conv halved the *grid size* from `14x14` to `7x7`, and we doubled the *number of filters* from 8 to 16, resulting in no overall change in the amount of computation. If we left the number of channels the same in each stride 2 layer, the amount of computation being done in the net would get less and less as it gets deeper. But we know that the deeper layers have to compute semantically rich features (such as eyes, or fur), so we wouldn't expect that doing *less* compute would make sense."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Receptive fields"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Another way to think of this is based on *receptive fields*. The \"receptive field\" is the area of an image that is involved in the calculation of a layer. On the book website, you'll find an Excel spreadsheet called `conv-example.xlsx` that shows the calculation of two stride 2 convolutional layers using an MNIST digit. Each layer has a single kernel. If we click on one of the cells in the *conv2* section, which shows the output of the second convolutional layer, and click *trace precendents*, we see this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Immediate](\"images/att_00068.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, the green cell is the cell we clicked on, and the blue highlighted cells are its *precedents*--that is, the cells used to calculate its value. These cells are the corresponding 3x3 area of cells from the input layer (on the left), and the cells from the filter (on the right). Let's now click *show precedents* again, to show what cells are used to calculate these inputs, and see what happens:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Secondary](\"images/att_00069.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example, we just have two convolutional layers, each of stride 2, so this is now tracing right back to the input image. We can see that a 7x7 area of cells in the input layer is used to calculate the single green cell in the Conv2 layer. This 7x7 area is the *receptive field* in the Input of the green activation in Conv2. We can also see that a second filter kernel is needed now, since we have two layers.\n",
+ "\n",
+ "As you see from this example, the deeper we are in the network (specfically, the more stride 2 convs we have before a layer), the larger the receptive field for an activation in that layer. A large receptive field means that a large amount of the input image is used to calculate each activation in that layer. So we know now that in the deeper layers of the network, we have semantically rich features, corresponding to larger receptive fields. Therefore, we'd expect that we'd need more weights for each of our features to handle this increasing complexity. This is another way of seeing the same thing we saw in the previous section: when we introduce a stride 2 conv in our network, we should also increase the number of channels."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### A note about twitter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are not, to say the least, big users of social networks in general. But our goal of this book is to help you become the best deep learning practitioner you can, and we would be remiss not to mention how important Twitter has been in our own deep learning journeys.\n",
+ "\n",
+ "You see, there's another part of Twitter, far away from Donald Trump and the Kardashians, which is the part of Twitter where deep learning researchers and practitioners talk shop every day. As we were writing the section above, Jeremy wanted to double-check to ensure that what we were saying about stride 2 convolutions was accurate, so he asked on twitter:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"twitter](\"images/att_00064.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A few minutes later, this answer popped up:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"twitter](\"images/att_00065.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Christian Szegedy is the first author of [Inception](https://arxiv.org/pdf/1409.4842.pdf), the 2014 Imagenet winner and source of many key insights used in modern neural networks. Two hours later, this appeared:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"twitter](\"images/att_00066.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do you recognize that name? We saw a picture of him back in <>, when we were talking about the Turing Award winners who set the foundation of deep learning today!\n",
+ "\n",
+ "Jeremy also asked on Twitter for help checking our description of label smoothing in <> was accurate, and got a response from again from directly from Christian Szegedy (label smoothing was originally introduced in the Inception paper):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"twitter](\"images/att_00067.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Many of the top people in deep learning today are Twitter regulars, and are very open about interacting with the wider community. One good way to get started is to look at a list of Jeremy's [recent Twitter likes](https://twitter.com/jeremyphoward/likes), or [Sylvain's](https://twitter.com/GuggerSylvain/likes). That way, you can see a list of Twitter users that we thought had interesting and useful things to say.\n",
+ "\n",
+ "Twitter is the main way we both stay up to date with interesting papers, software releases, and other deep learning news. For making connections with the deep learning community, we recommend getting involved both in the [fast.ai forums](https://forums.fast.ai) and Twitter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Colour images"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A colour picture is a rank-3 tensor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([3, 1000, 846])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im = image2tensor(Image.open('images/grizzly.jpg'))\n",
+ "im.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a 5x5 input, and 4x4 kernel, and 2 pixels of padding, we end up with a 6x6 activation map:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we add a kernel of size `ks` by `ks` (with `ks` an odd number), the necessary padding on each side to keep the same shape is `ks//2`. An even number for `ks` would require a different amount of padding on the top/bottom, left/right, but in practice we almost never use an even filter size.\n",
+ "\n",
+ "So far, when we have applied the kernel to the grid, we have moved it one pixel over at a time. But we can jump further; for instance, we could move over two pixels after each kernel application. This is known as a *stride 2* convolution. The most common kernel size in practice is 3x3, and the most common padding is 1. As you'll see, stride 2 convolutions are useful for decreasing the size of our outputs, and stride 1 convolutions are useful for adding layers without changing the output size."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In an image of size `h` by `w` like before, using a padding of 1 and a stride of 2 will give us a result of size `(h+1)//2` by `(w+1)//2`. The general formula for each dimension is `(n + 2*pad - ks)//stride + 1` where `pad` is the padding, `ks` the size of our kernel and `stride` the stride."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### CNNs from different viewpoints"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "fast.ai student Matt Kleinsmith came up with the very clever idea of showing [CNNs from different viewpoints](https://medium.com/impactai/cnns-from-different-viewpoints-fab7f52d159c). In fact, it's so clever, and so helpful, we're going to show it here too!\n",
+ "\n",
+ "Here's our 3x3 pixel *image*, with each *pixel* labeled with a letter:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...and our kernel, with each weight labeled with a greek letter:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since the filter fits in the image four times, we have four results:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here’s how we applied the kernel to each section of the image to yield each result:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The equation view:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that the bias term, b, is the same for each section of the image. You can consider the bias as part of the filter, just like the weights (α, β, γ, δ) are part of the filter.\n",
+ "\n",
+ "The compact equation view:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's an interesting insight -- a convolution can be represented as a special kind of matrix multiplication. The weight matrix is just like the ones from traditional neural networks. However, this weight matrix has two special properties:\n",
+ "\n",
+ "1. The zeros shown in gray are untrainable. This means that they’ll stay zero throughout the optimization process.\n",
+ "1. Some of the weights are equal, and while they are trainable (i.e. changeable), they must remain equal. These are called *shared weights*.\n",
+ "\n",
+ "The zeros correspond to the pixels that the filter can't touch. Each row of the weight matrix corresponds to one application of the filter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Our first convolutional neural network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Learning kernels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There is no reason to believe that these particular edge filters are the most useful kernels for image recognition. Furthermore, we've seen that in later layers convolutional kernels become complex transformations of features from lower levels — we do not have a good idea of how to manually construct these.\n",
+ "\n",
+ "Instead, it would be best to learn the values of the kernels. We already know how to do this — SGD! In effect, the model will learn the features that are useful for classification.\n",
+ "\n",
+ "When we use convolutions instead of (or in addition to) regular linear layers we create a *convolutional neural network*, or *CNN*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating the CNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's the basic neural network we had in <| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "0.072684 | \n", + "0.045110 | \n", + "0.990186 | \n", + "00:05 | \n", + "
| 1 | \n", + "0.022580 | \n", + "0.030775 | \n", + "0.990186 | \n", + "00:05 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, the green cell is the cell we clicked on, and the blue highlighted cells are its *precedents*--that is, the cells used to calculate its value. These cells are the corresponding 3x3 area of cells from the input layer (on the left), and the cells from the filter (on the right). Let's now click *show precedents* again, to show what cells are used to calculate these inputs, and see what happens:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example, we just have two convolutional layers, each of stride 2, so this is now tracing right back to the input image. We can see that a 7x7 area of cells in the input layer is used to calculate the single green cell in the Conv2 layer. This 7x7 area is the *receptive field* in the Input of the green activation in Conv2. We can also see that a second filter kernel is needed now, since we have two layers.\n",
+ "\n",
+ "As you see from this example, the deeper we are in the network (specfically, the more stride 2 convs we have before a layer), the larger the receptive field for an activation in that layer. A large receptive field means that a large amount of the input image is used to calculate each activation in that layer. So we know now that in the deeper layers of the network, we have semantically rich features, corresponding to larger receptive fields. Therefore, we'd expect that we'd need more weights for each of our features to handle this increasing complexity. This is another way of seeing the same thing we saw in the previous section: when we introduce a stride 2 conv in our network, we should also increase the number of channels."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### A note about twitter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are not, to say the least, big users of social networks in general. But our goal of this book is to help you become the best deep learning practitioner you can, and we would be remiss not to mention how important Twitter has been in our own deep learning journeys.\n",
+ "\n",
+ "You see, there's another part of Twitter, far away from Donald Trump and the Kardashians, which is the part of Twitter where deep learning researchers and practitioners talk shop every day. As we were writing the section above, Jeremy wanted to double-check to ensure that what we were saying about stride 2 convolutions was accurate, so he asked on twitter:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A few minutes later, this answer popped up:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Christian Szegedy is the first author of [Inception](https://arxiv.org/pdf/1409.4842.pdf), the 2014 Imagenet winner and source of many key insights used in modern neural networks. Two hours later, this appeared:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Do you recognize that name? We saw a picture of him back in <"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Many of the top people in deep learning today are Twitter regulars, and are very open about interacting with the wider community. One good way to get started is to look at a list of Jeremy's [recent Twitter likes](https://twitter.com/jeremyphoward/likes), or [Sylvain's](https://twitter.com/GuggerSylvain/likes). That way, you can see a list of Twitter users that we thought had interesting and useful things to say.\n",
+ "\n",
+ "Twitter is the main way we both stay up to date with interesting papers, software releases, and other deep learning news. For making connections with the deep learning community, we recommend getting involved both in the [fast.ai forums](https://forums.fast.ai) and Twitter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Colour images"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A colour picture is a rank-3 tensor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([3, 1000, 846])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im = image2tensor(Image.open('images/grizzly.jpg'))\n",
+ "im.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_image(im);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The first axis contains the channels: red, green, and blue:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "_,axs = subplots(1,3)\n",
+ "for bear,ax,color in zip(im,axs,('Reds','Greens','Blues')):\n",
+ " show_image(255-bear, ax=ax, cmap=color)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We saw what the convolution operation was for one filter on one channel of the image (our examples were done on a square). A convolution layer will take an image with a certain number of channels (3 for the first layer for regular RGB color images) and output an image with a different number of channels. Like our hidden size that represented the numbers of neurons in a linear layer, we can decide to have has many filters as we want, and each of them will be able to specialize, some to detect horizontal edges, other to detect vertical edges and so forth, to give something like we studied in <>.\n",
+ "\n",
+ "On one sliding window, we have a certain number of channels and we need as many filters (we don't use the same kernel for all the channels). So our kernel doesn't have a size of 3 by 3, but `ch_in` (for channel in) by 3 by 3. On each channel, we multiply the elements of our window by the elements of the coresponding filter then sum the results (as we saw before) and sum over all the filters. In the following example, the result of our conv layer on that window is $y_{R} + y_{G} + y_{B}$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Convolution](\"images/chapter9_rgbconv.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, in order to apply a convolution to a colour picture we require a kernel tensor with a matching size as the first axis. At each location, the corresponding parts of the kernel and the image patch are multiplied together.\n",
+ "\n",
+ "These are then all added together, to produce a single number, for each grid location, for each output feature:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Adding](\"images/chapter9_rgb_conv_stack.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we have `ch_out` filters like this, so in the end, the result of our convolutional layer will be a batch of images with `ch_out` channels and a height and width given by the formula above. This give us `ch_out` tensors of size `ch_in x ks x ks` that we represent in one big tensor of 4 dimensions. In PyTorch, the order of the dimensions for those weights is `ch_out x ch_in x ks x ks`.\n",
+ "\n",
+ "Additionally, we may want to have a bias for each filter. In the example above, the final result for our convolutional layer would be $y_{R} + y_{G} + y_{B} + b$ in that case. Like in a linear layer, there are as many bias as we have kernels, so the bias is a vector of size `ch_out`.\n",
+ "\n",
+ "There are no special mechanisms required when setting up a CNN for training with color images. Just make sure your first layer as 3 inputs.\n",
+ "\n",
+ "There are lots of ways of processing color images. For instance, you can change them to black and white, or change from RGB to HSV color space, and so forth. In general, it turns out experimentally that changing the encoding of colors won't make any difference to your model results, as long as you don't lose information in the transformation. So transforming to black and white is a bad idea, since it removes the color information entirely (and this can be critical; for instance a pet breed may have a distinctive color); but converting to HSV generally won't make any difference.\n",
+ "\n",
+ "Now you know what those pictures in <> of \"what a neural net learns\" from the Zeiler and Fergus paper mean! This is their picture of some of the layer 1 weights which we showed:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Layer](\"images/att_00031.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is taking the 3 slices of the convolutional kernel, for each output feature, and displaying them as images. We can see that even although the creators of the neural net never explicitly created kernels to find edges, for instance, the neural net automatically discovered these features using SGD."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've seen that convolutions are just a type of matrix multiplication, with two constraints on the weight matrix: some elements are always zero, and some elements are tied (forced to always have the same value). In <> we saw the eight requirements from the 1986 book *Parallel Distributed Processing*; one of them was \"A pattern of connectivity among units\". That's exactly what these constraints do: they enforce a certain pattern of connectivity.\n",
+ "\n",
+ "These constraints allow us to use far less parameters in our model, without sacrificing the ability to represent complex visual features. That means we can train deeper models faster, with less over-fitting. Although the universal approximation theorem shows that it should be *possible* to represent anything in a fully connected network in one hidden layer, we've seen now that in *practice* we can train much better models by being thoughtful about network architecture.\n",
+ "\n",
+ "Convolutions are by far the most common pattern of connectivity we see in neural nets (along with regular linear layers, which we refer to as *fully connected*), but it's likely that many more will be discovered."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Questionnaire"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What is a \"feature\"?\n",
+ "1. Write out the convolutional kernel matrix for a top edge detector.\n",
+ "1. Write out the mathematical operation applied by a 3 x 3 kernel to a single pixel in an image.\n",
+ "1. What is the value of a convolutional kernel apply to a 3 x 3 matrix of zeros?\n",
+ "1. What is padding?\n",
+ "1. What is stride?\n",
+ "1. Create a nested list comprehension to complete any task that you choose.\n",
+ "1. What are the shapes of the input and weight parameters to PyTorch's 2D convolution?\n",
+ "1. What is a channel?\n",
+ "1. What is the relationship between a convolution and a matrix multiplication?\n",
+ "1. What is a convolutional neural network?\n",
+ "1. What is the benefit of refactoring parts of your neural network definition?\n",
+ "1. What is `Flatten`? Where does it need to be included in the MNIST CNN? Why?\n",
+ "1. What does \"NCHW\" mean?\n",
+ "1. Why does the third layer of the MNIST CNN have `7*7*(1168-16)` multiplications?\n",
+ "1. What is a receptive field?\n",
+ "1. What is the size of the receptive field of an activation after two stride 2 convolutions? Why?\n",
+ "1. Run conv-example.xlsx yourself and experiment with \"trace precedents\".\n",
+ "1. Have a look at Jeremy or Sylvain's list of recent Twitter \"like\"s, and see if you find any interesting resources or ideas there.\n",
+ "1. How is a color image represented as a tensor?\n",
+ "1. How does a convolution work with a color input?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further research"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. What features other than edge detectors have been used in computer vision (especially before deep learning became popular)?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "split_at_heading": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/14_deep_conv.ipynb b/14_deep_conv.ipynb
new file mode 100644
index 000000000..990dfc526
--- /dev/null
+++ b/14_deep_conv.ipynb
@@ -0,0 +1,1069 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "from utils import *"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "[[chapter_deep_conv]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Improving training stability"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since we are so good at recognizing threes from sevens, let's move onto something harder—recognized all 10 digits. That means we'll need to use `MNIST` instead of `MNIST_SAMPLE`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = untar_data(URLs.MNIST)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#hide\n",
+ "Path.BASE_PATH = path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(#2) [Path('testing'),Path('training')]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The data is in two folders named `training` and `testing`, so we have to tell `GrandparentSplitter` about that (it defaults to `train` and `valid`). We define a function `get_dls` to make it easy to change our batch size later:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_dls(bs=64):\n",
+ " return DataBlock(\n",
+ " blocks=(ImageBlock(cls=PILImageBW), CategoryBlock), \n",
+ " get_items=get_image_files, \n",
+ " splitter=GrandparentSplitter('training','testing'),\n",
+ " get_y=parent_label,\n",
+ " batch_tfms=Normalize()\n",
+ " ).dataloaders(path, bs=bs)\n",
+ "\n",
+ "dls = get_dls()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Always a good idea to look at your data before you use it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, in order to apply a convolution to a colour picture we require a kernel tensor with a matching size as the first axis. At each location, the corresponding parts of the kernel and the image patch are multiplied together.\n",
+ "\n",
+ "These are then all added together, to produce a single number, for each grid location, for each output feature:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we have `ch_out` filters like this, so in the end, the result of our convolutional layer will be a batch of images with `ch_out` channels and a height and width given by the formula above. This give us `ch_out` tensors of size `ch_in x ks x ks` that we represent in one big tensor of 4 dimensions. In PyTorch, the order of the dimensions for those weights is `ch_out x ch_in x ks x ks`.\n",
+ "\n",
+ "Additionally, we may want to have a bias for each filter. In the example above, the final result for our convolutional layer would be $y_{R} + y_{G} + y_{B} + b$ in that case. Like in a linear layer, there are as many bias as we have kernels, so the bias is a vector of size `ch_out`.\n",
+ "\n",
+ "There are no special mechanisms required when setting up a CNN for training with color images. Just make sure your first layer as 3 inputs.\n",
+ "\n",
+ "There are lots of ways of processing color images. For instance, you can change them to black and white, or change from RGB to HSV color space, and so forth. In general, it turns out experimentally that changing the encoding of colors won't make any difference to your model results, as long as you don't lose information in the transformation. So transforming to black and white is a bad idea, since it removes the color information entirely (and this can be critical; for instance a pet breed may have a distinctive color); but converting to HSV generally won't make any difference.\n",
+ "\n",
+ "Now you know what those pictures in <"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is taking the 3 slices of the convolutional kernel, for each output feature, and displaying them as images. We can see that even although the creators of the neural net never explicitly created kernels to find edges, for instance, the neural net automatically discovered these features using SGD."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We've seen that convolutions are just a type of matrix multiplication, with two constraints on the weight matrix: some elements are always zero, and some elements are tied (forced to always have the same value). In <"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls.show_batch(max_n=9, figsize=(4,4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have our data ready, we can train a simple model on it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A simple baseline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the previous chapter, we built a model based on a `conv` function like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def conv(ni, nf, ks=3, act=True):\n",
+ " res = nn.Conv2d(ni, nf, stride=2, kernel_size=ks, padding=ks//2)\n",
+ " if act: res = nn.Sequential(res, nn.ReLU())\n",
+ " return res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's start with a basic CNN as a baseline. We'll use the same as we had in the last chapter, but with one tweak: we'll use more activations.\n",
+ "\n",
+ "As we discussed, we generally want to double the number of filters each time we have a stride 2 layer. So, one way to increase the number of filters throughout our network is to double the number of activations in the first layer them – then every layer after that will end up twice as big as the previous version as well.\n",
+ "\n",
+ "But there is a subtle problem with this. Consider the kernel which is being applied to each pixel. By default, we use a 3x3 pixel kernel. That means that there are a total of 3×3 = 9 pixels that the kernel is being applied to at each location. Previously, our first layer had four filters output. That meant that there were four values being computed from nine pixels at each location. Think about what happens if we double this output to 8 filters. Then when we apply our kernel we would be using nine pixels to calculate eight numbers. That means that it isn't really learning much at all — the output size is almost the same as the input size. Neural networks will only create useful features if they're forced to do so—that is, that the number of outputs from an operation is smaller than the number of inputs.\n",
+ "\n",
+ "To fix this, we can use a larger kernel in the first layer. If we use a kernel of 5x5 pixels then there are 25 pixels being used at each kernel application — creating eight filters from this will mean the neural net will have to find some useful features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def simple_cnn():\n",
+ " return sequential(\n",
+ " conv(1 ,8, ks=5), #14x14\n",
+ " conv(8 ,16), #7x7\n",
+ " conv(16,32), #4x4\n",
+ " conv(32,64), #2x2\n",
+ " conv(64,10, act=False), #1x1\n",
+ " Flatten(),\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you'll see in a moment, we're going to look inside our models while they're training, in order to try to find ways to make them train better. To do this, we use the `ActivationStats` callback, which records the mean, standard deviation, and histogram of activations of every trainable layer (as we've seen, callbacks are used to add behavior to the training loop; we'll see how they work in <>)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai2.callback.hook import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We want to train quickly, so that means training at a high learning rate. Let's see how we go at 0.06:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def fit(epochs=1):\n",
+ " learn = Learner(dls, simple_cnn(), loss_func=F.cross_entropy,\n",
+ " metrics=accuracy, cbs=ActivationStats(with_hist=True))\n",
+ " learn.fit(epochs, 0.06)\n",
+ " return learn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "