An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
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This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic.
In this guide our goal will be to write a program that uses machine learning to predict, with a high degree of certainty, whether the images in data/untrained-samples are of dolphins or seahorses using only the images themselves, and without having seen them before. Here are two example images we'll use:
To do that we’re going to train and use a Convolutional Neural Network (CNN). We’re going to approach this from the point of view of a practitioner vs. from first principles. There is so much excitement about AI right now, but much of what’s being written feels like being taught to do tricks on your bike by a physics professor at a chalkboard instead of your friends in the park.
I’ve decided to write this on Github vs. as a blog post because I’m sure that some of what I’ve written below is misleading, naive, or just plain wrong. I’m still learning myself, and I’ve found the lack of solid beginner documentation an obstacle. If you see me making a mistake or missing important details, please send a pull request.
With all of that out the way, let me show you how to do some tricks on your bike!
Here’s what we’re going to explore:
This guide won’t teach you how neural networks are designed, cover much theory, or use a single mathematical expression. I don’t pretend to understand most of what I’m going to show you. Instead, we’re going to use existing things in interesting ways to solve a hard problem.
Q: "I know you said we won’t talk about the theory of neural networks, but I’m feeling like I’d at least like an overview before we get going. Where should I start?"
There are literally hundreds of introductions to this, from short posts to full online courses. Depending on how you like to learn, here are three options for a good starting point:
Installing the software we'll use (Caffe and DIGITS) can be frustrating, depending on your platform and OS version. By far the easiest way to do it is using Docker. Below we examine how to do it with Docker, as well as how to do it natively.
First, we’re going to be using the Caffe deep learning framework from the Berkely Vision and Learning Center (BSD licensed).
Q: “Wait a minute, why Caffe? Why not use something like TensorFlow, which everyone is talking about these days…”
There are a lot of great choices available, and you should look at all the options. TensorFlow is great, and you should play with it. However, I’m using Caffe for a number of reasons:
But the number one reason I’m using Caffe is that you don’t need to write any code to work with it. You can do everything declaratively (Caffe uses structured text files to define the network architecture) and using command-line tools. Also, you can use some nice front-ends for Caffe to make training and validating your network a lot easier. We’ll be using nVidia’s DIGITS tool below for just this purpose.
Caffe can be a bit of work to get installed. There are installation instructions for various platforms, including some prebuilt Docker or AWS configurations.
NOTE: when making my walkthrough, I used the following non-released version of Caffe from their Github repo: https://github.com/BVLC/caffe/commit/5a201dd960840c319cefd9fa9e2a40d2c76ddd73
On a Mac it can be frustrating to get working, with version issues halting your progress at various steps in the build. It took me a couple of days of trial and error. There are a dozen guides I followed, each with slightly different problems. In the end I found this one to be the closest. I’d also recommend this post, which is quite recent and links to many of the same discussions I saw.
Getting Caffe installed is by far the hardest thing we'll do, which is pretty neat, since you’d assume the AI aspects would be harder! Don’t give up if you have issues, it’s worth the pain. If I was doing this again, I’d probably use an Ubuntu VM instead of trying to do it on Mac directly. There's also a Caffe Users group, if you need answers.
Q: “Do I need powerful hardware to train a neural network? What if I don’t have access to fancy GPUs?”
It’s true, deep neural networks require a lot of computing power and energy to train...if you’re training them from scratch and using massive datasets. We aren’t going to do that. The secret is to use a pretrained network that someone else has already invested hundreds of hours of compute time training, and then to fine tune it to your particular dataset. We’ll look at how to do this below, but suffice it to say that everything I’m going to show you, I’m doing on a year old MacBook Pro without a fancy GPU.
As an aside, because I have an integrated Intel graphics card vs. an nVidia GPU, I decided to use the OpenCL Caffe branch, and it’s worked great on my laptop.
When you’re done installing Caffe, you should have, or be able to do all of the following:
build/ dir which contains everything you need to run caffe,
the Python bindings, etc. The parent dir that contains build/ will be your
CAFFE_ROOT (we’ll need this later).make test && make runtest should passpip install -r requirements.txt in python/),
running make pycaffe && make pytest should passmake distribute in order to create a distributable version of caffe with all necessary headers, binaries, etc. in distribute/.On my machine, with Caffe fully built, I’ve got the following basic layout in my CAFFE_ROOT dir:
caffe/
build/
python/
lib/
tools/
caffe ← this is our main binary
distribute/
python/
lib/
include/
bin/
proto/
At this point, we have everything we need to train, test, and program with neural networks. In the next section we’ll add a user-friendly, web-based front end to Caffe called DIGITS, which will make training and testing our networks much easier.
nVidia’s Deep Learning GPU Training System, or DIGITS, is BSD-licensed Python web app for training neural networks. While it’s possible to do everything DIGITS does in Caffe at the command-line, or with code, using DIGITS makes it a lot easier to get started. I also found it more fun, due to the great visualizations, real-time charts, and other graphical features. Since you’re experimenting and trying to learn, I highly recommend beginning with DIGITS.
There are quite a few good docs at https://github.com/NVIDIA/DIGITS/tree/master/docs, including a few Installation, Configuration, and Getting Started pages. I’d recommend reading through everything there before you continue, as I’m not an expert on everything you can do with DIGITS. There's also a public DIGITS User Group if you have questions you need to ask.
There are various ways to install and run DIGITS, from Docker to pre-baked packages on Linux, or you can build it from source. I’m on a Mac, so I built it from source.
NOTE: In my walkthrough I've used the following non-released version of DIGITS from their Github repo: https://github.com/NVIDIA/DIGITS/commit/81be5131821ade454eb47352477015d7c09753d9
Because it’s just a bunch of Python scripts, it was fairly painless to get working.
The one thing you need to do is tell DIGITS where your CAFFE_ROOT is by setting
an environment variable before starting the server:
export CAFFE_ROOT=/path/to/caffe
./digits-devserver
NOTE: on Mac I had issues with the server scripts assuming my Python binary was
called python2, where I only have python2.7. You can symlink it in /usr/bin
or modify the DIGITS startup script(s) to use the proper binary on your system.
Once the server is started, you can do everything else via your web browser at http://localhost:5000, which is what I'll do below.
Install Docker, if not already installed, then run the following command in order to pull and run a full Caffe + Digits container. A few things to note:
/path/to/this/repository to the location of this cloned repo, and /data/repo within the container
will be bound to this directory. This is useful for accessing the images discussed below.docker run --name digits -d -p 8080:5000 -v /path/to/this/repository:/data/repo kaixhin/digits
Now that we have our container running you can open up your web browser and open http://localhost:8080. Everything in the repository is now in the container directory /data/repo. That's it. You've now got Caffe and DIGITS working.
If you need shell access, use the following command:
docker exec -it digits /bin/bash
Training a neural network involves a few steps:
We’re going to do this 3 different ways, in order to show the difference betwee
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