Learning Deep Learning With Keras

5/20/2017

LearningDeepLearningwithKeras

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Learning Deep Learning with Keras 30 Apr 2017 • Piotr Migdał • [machine-learning] [deep-learning] [overview] see: tweet by François Chollet (the creator of Keras) with over 140 retweets see: Facebook post by Kaggle with over 200 shares see: like it? upvote it on the Hacker News :)

I teach deep learning both for a living (as the main deepsense.io instructor, in a Kagglewinning team1) and as a part of my volunteering with the Polish Children’s Fund giving workshops to gifted high-school students2. I want to share a few things I’ve learnt about teaching (and learning) deep learning. Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over3, this post is for you! Its goal is not to teach neural networks by itself, but to provide an overview and to point to didactically useful resources.

http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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Don’t be afraid of artificial neural networks - it is easy to start! In fact, my biggest regret is delaying learning it, because of the perceived difficulty. To start, all you need is really basic programming, very simple mathematics and knowledge of a few machine learning concepts. I will explain where to start with these requirements. In my opinion, the best way to start is from a high-level interactive approach (see also: Quantum mechanics for high-school students and my Quantum Game with Photons). For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python. If you like to train neural networks with less code than in Keras, the only viable option is to use pigeons. Yes, seriously: pigeons spot cancer as well as human experts!

What is deep learning and why is it cool? Deep learning is a name for machine learning techniques using many-layered artificial neural networks. Occasionally people use the term artificial intelligence, but unless you

want to sound sci-fi, it is reserved for problems that are currently considered “too hard for machines” - a frontier that keeps moving rapidly. This is a field that exploded in the last few years, reaching human-level accuracy in visual recognition tasks (among many other tasks). Unlike quantum computing, or nuclear fusion - it is a technology that is being applied right now, not some possibility for the future. There is a rule of thumb: Pretty much anything that a normal person can do in <1 sec, we can now automate with AI. - Andrew Ng’s tweet

Some people go even further, extrapolating that statement to experts. It’s not a surprise that companies like Google and Facebook at the cutting-edge of progress. In fact, every few months I am blown away by something exceeding my expectations, e.g.: The Unreasonable Effectiveness of Recurrent Neural Networks4 for generating fake Shakespeare, Wikipedia entries and LaTeX articles A Neural Algorithm of Artistic Style style transfer (and for videos!) Real-time Face Capture and Reenactment Colorful Image Colorization Plug & Play Generative Networks for photorealistic image generation Dermatologist-level classification of skin cancer along with other medical diagnostic tools Image-to-Image Translation (pix2pix) - sketch to photo Teaching Machines to Drawsketches of cats, dogs etc It looks like some sorcery. If you are curious what neural networks are, take a look at this series of videos for a smooth introduction: Neural Networks Demystified by Stephen Welch - video series A Visual and Interactive Guide to the Basics of Neural Networks by J Alammar http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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These techniques are data-hungry. See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):

In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.

Let’s play! Do I need some Skynet to run it? Actually not - it’s a piece of software, like any other. And you can even play with it in your browser: TensorFlow Playground for point separation, with a visual interface ConvNetJS for digit and image recognition Keras.js Demo - to visualize and use real networks in your browser (e.g. ResNet-50) Or… if you want to use Keras in Python, see this minimal example - just to get convinced you can use it on your own computer.

Python and machine learning I mentioned basics Python and machine learning as a requirement. They are already covered in my introduction to data science in Python and statistics and machine learning sections, respectively. For Python, if you already have Anaconda distribution (covering most data science packages), the only thing you need is to install TensorFlow and Keras. When it comes to machine learning, you don’t need to learn many techniques before jumping into deep learning. Though, later it would be a good practice to see if a given problem can be solved with much simpler methods. For example, random forest is often a lockpick, working out-of-the-box for many problems. You need to understand why we need to http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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train and then test a classifier (to validate its predictive power). To get the gist of it, start with this beautiful tree-based animation: Visual introduction to machine learning by Stephanie Yee and Tony Chu Also, it is good to understand logistic regression, which is a building block of almost any neural network for classification.

Mathematics Deep learning (that is - neural networks with many layers) uses mostly very simple mathematical operations - just many of them. Here there are a few, which you can find in almost any network (look at this list, but don’t get intimidated): vectors, matrices, multi-dimensional arrays, addition, multiplication, convolutions to extract and process local patterns, activation functions: sigmoid, tanh or ReLU to add non-linearity, softmax to convert vectors into probabilities, log-loss (cross-entropy) to penalize wrong guesses in a smart way (see also KullbackLeibler Divergence Explained), gradients and chain-rule (backpropagation) for optimizing network parameters, stochastic gradient descent and its variants (e.g. momentum). If your background is in mathematics, statistics, physics5 or signal processing - most likely you already know more than enough to start! If your last contact with mathematics was in high-school, don’t worry. Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet. It is only a proof-of-principle solution - not only inefficient, but also lacking the most crucial part - the ability to train new networks. The basics of vector calculus are crucial not only for deep learning, but also for many other machine learning techniques (e.g. in word2vec I wrote about). To learn it, I recommend starting from one of the following: J. Ström, K. Åström, and T. Akenine-Möller, Immersive Linear Algebra - a linear algebra book with fully interactive figures Applied Math and Machine Learning Basics: Linear Algebra from the Deep Learning book Linear algebra cheat sheet for deep learning by Brendan Fortuner Since there are many references to NumPy, it may be useful to learn its basics: From Python to Numpy by Nicolas P. Rougier http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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SciPy lectures: The NumPy array object At the same time - look back at the meme, at the What mathematicians think I do part. It’s totally fine to start from a magically working code, treating neural network layers like LEGO blocks.

Frameworks There is a handful of popular deep learning libraries, including TensorFlow, Theano, Torch and Caffe. Each of them has Python interface (now also for Torch: PyTorch). So, which to choose? First, as always, screw all subtle performance benchmarks, as premature optimization is the root of all evil. What is crucial is to start with one which is easy to write (and read!), one with many online resources, and one that you can actually install on your computer without too much pain. Bear in mind that core frameworks are multidimensional array expression compilers with GPU support. Current neural networks can be expressed as such. However, if you just want to work with neural networks, by rule of least power, I recommend starting with a framework just for neural networks. For example…

Keras If you like the philosophy of Python (brevity, readability, one preferred way to do things), Keras is for you. It is a high-level library for neural networks, using TensorFlow or Theano as its backend. Also, if you want to have a propaganda picture, there is a possibly biased (or overfitted?) popularity ranking: The state of deep learning frameworks (from GitHub metrics), April 2017. - François Chollet (Keras creator)

If you want to consult a different source, based on arXiv papers rather than GitHub activity, see A Peek at Trends in Machine Learningby Andrej Karpathy. Popularity is important - it means that if you want to search for a network architecture, googling for it (e.g. UNet Keras ) is likely to return an example. Where to start learning it? Documentation on Keras is nice, and its blog is a valuable resource. For a complete, interactive introduction to deep learning with Keras in Jupyter Notebook, I really recommend: http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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Deep Learning with Keras and TensorFlowby Valerio Maggio For shorter ones, try one of these: Visualizing parts of Convolutional Neural Networks using Keras and Cats by Erik Reppel Deep learning for complete beginners: convolutional neural networks with Keras by Petar Veličković Handwritten Digit Recognition using Convolutional Neural Networks in Python with 6

Keras by Jason Brownlee (Theano tensor dimension order ) There are a few add-ons to Keras, which are especially useful for learning it. I created ASCII summary for sequential models to show data flow inside networks (in a nicer way than model.summary() ). It shows layers, dimensions of data (x, y, channels) and the number of free parameters (to be optimized). For example, for a network for digit recognition it might look like: OPERATION Input Conv2D relu Conv2D relu MaxPooling2D Dropout Conv2D relu Conv2D relu MaxPooling2D Dropout Flatten Dense relu Dropout Dense softmax

DATA DIMENSIONS

WEIGHTS(N)

##### \|/ ##### \|/ ##### Y max ##### | || ##### \|/ #####

32 32 3 ------------------32 32 32 ------------------30 30 32 ------------------15 15 32 ------------------15 15 32 ------------------15 15 64

\|/ ##### Y max ##### | || ##### ||||| ##### XXXXX ##### | || ##### XXXXX #####

------------------13 13 64 ------------------6 6 64 ------------------6 6 64 ------------------2304 ------------------512 ------------------512 ------------------10

WEIGHTS(%

896

0.1%

9248

0.7%

0

0.0%

0

0.0%

18496

1.5%

36928

3.0%

0

0.0%

0

0.0%

0

0.0%

1180160

94.3%

0

0.0%

5130

0.4%

You might be also interested in nicer progress bars with keras-tqdm, exploration of activations at each layer with quiver or converting Keras models to JavaScript, runnable in a http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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browser with Keras.js.

TensorFlow If not Keras, then I recommend starting with bare TensorFlow. It is a bit more low-level and verbose, but makes it straightforward to optimize various multidimensional array (or, well, tensor) operations. A few good resources: the official TensorFlow Tutorial is very good Learn TensorFlow and deep learning, without a Ph.D. by Martin Görner TensorFlow Tutorial and Examples for beginnersby Aymeric Damien (with Python 2.7) Simple tutorials using Google’s TensorFlow Framework by Nathan Lintz In any case, TensorBoard makes it easy to keep track of the training process. It can also be used with Keras, via callbacks.

Other Theano is similar to TensorFlow, but a bit older and harder to start. For example, you need to manually write updates of variables. Typical neural network layers are not included, so one often uses libraries such as Lasagne. If you’re looking for a place to start, I like this introduction: Theano Tutorial by Marek Rei At the same time, if you see some nice code in Torch or PyTorch, don’t be afraid to install and run it!

Datasets Every machine learning problem needs data. You cannot just tell it “detect if there is a cat in this picture” and expect the computer to tell you the answer. You need to show many

instances of cats, and pictures not containing cats, and (hopefully) it will learn to generalize it to other cases. So, you need some data to start. And it is not a drawback of machine learning or just deep learning - it is a fundamental property of any learning! Before you dive into uncharted waters, it is good to take a look at some popular datasets. The key part about them is that they are… popular. It means that you can find a lot of examples what works. And have a guarantee that these problems can be solved with neural networks.

MNIST Many good ideas will not work well on MNIST (e.g. batch norm). Inversely many bad ideas may work on MNIST and no[t] transfer to real [computer vision]. François - Chollet’s tweet http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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Still, I recommend starting with the MNIST digit recognition dataset (60k grayscale 28x28 images), included in keras.datasets. Not necessary to master it, but just to get a sense that it works at all (or to test the basics of Keras on your local machine).

notMNIST Indeed, I once even proposed that the toughest challenge facing AI workers is to answer the question: “What are the letters ‘A’ and Douglas ‘I’? - R. Hofstadter (1995)

A more interesting dataset, and harder for classical machine learning algorithms, is notMNIST (letters A-J from strange fonts). If you want to start with it, here is my code for notMNIST loading and logistic regression in Keras.

CIFAR If you want to play with image recognition, there is CIFAR dataset, a dataset of 32x32 photos (also in keras.datasets). It comes in two versions: 10 simple classes (including cats, dogs, frogs and airplanes ) and 100 harder and more nuanced classes (including beaver, dolphin, otter, seal and whale). I strongly suggest starting with CIFAR-10, the simpler version. Beware, more complicated networks may take quite some time (~12h on CPU my 7 year old Macbook Pro).

More Deep learning requires a lot of data. If you want to train your network from scratch, it may require as many as ~10k images even if low-resolution (32x32). Especially if data is scarce, there is no guarantee that a network will learn anything. So, what are the ways to go? use really low res (if your eye can see it, no need to use higher resolution) get a lot of data (for images like 256x256 it may be: millions of instances) re-train a network that already saw a lot generate much more data (with rotations, shifts, distortions) Often, it’s a combination of everything mentioned here.

Standing on the shoulders of giants Creating a new neural network has a lot in common with cooking - there are typical ingredients (layers) and recipes (popular network architectures). The most important cooking contest is ImageNet Large Scale Visual Recognition Challenge, with recognition of hundreds of classes from half a million dataset of photos. Look at these Neural Network Architectures, typically using 224x224x3 input (chart by Eugenio Culurciello):


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Circle size represents the number of parameters (a lot!). It doesn’t mention SqueezeNet though, an architecture vastly reducing the number of parameters (e.g. 50x fewer). A few key networks for image classification can be readily loaded from the keras.applications module: Xception, VGG16, VGG19, ResNet50, InceptionV3. Some others are not as plug & play, but still easy to find online - yes, there is SqueezeNet in Keras. These networks serve two purposes: they give insight into useful building blocks and architectures they are great candidates for retraining (so-called transfer learning), when using architecture along with pre-trained weights) Some other important network architectures for images: U-Net: Convolutional Networks for Biomedical Image Segmentation Retina blood vessel segmentation with a convolution neural network - Keras implementation Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras A Neural Algorithm of Artistic Style Neural Style Transfer & Neural Doodles implemented in Keras by Somshubra Majumdar A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN by Dhruv Parthasarathy Another set of insights: http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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The Neural Network Zoo by Fjodor van Veen How to train your Deep Neural Network - how many layers, parameters, etc

Infrastructure For very small problems (e.g. MNIST, notMNIST), you can use your personal computer even if it is a laptop and computations are on CPU. For small problems (e.g. CIFAR, the unreasonable RNN), you might be still able to use a PC, but it requires much more patience and trade-offs. For medium and larger problems, essentially the only way to go is to use a machine with a strong graphic card (GPU). For example, it took us 2 days to train a model for satellite image processing for a Kaggle competition, see our: Deep learning for satellite imagery via image segmentation by Arkadiusz Nowaczyński On a strong CPU it would have taken weeks, see: Benchmarks for popular convolutional neural network models by Justin Johnson The easiest, and the cheapest, way to use a strong GPU is to rent a remote machine on a per-hour basis. You can use Amazon (it is not only a bookstore!), here are some guides: Keras with GPU on Amazon EC2 – a step-by-step instruction by Mateusz Sieniawski, my mentee Running Jupyter notebooks on GPU on AWS: a starter guide by Francois Chollet

Further learning I encourage you to interact with code. For example, notMNIST or CIFAR-10 can be great starting points. Sometimes the best start is to start with someone’s else code and run it, then see what happens when you modify parameters. For learning how it works, this one is a masterpiece: CS231n: Convolutional Neural Networks for Visual Recognition by Andrej Karpathy and the lecture videos When it comes to books, there is a wonderful one, starting from introduction to mathematics and machine learning learning context (it even covers log-loss and entropy in a way I like!): Deep Learning, An MIT Press book by Ian Goodfellow, Yoshua Bengio and Aaron Courville Alternatively, you can use (it may be good for an introduction with interactive materials, but I’ve found the style a bit long-winded): http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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Neural Networks and Deep Learning by Michael Nielsen

Other materials There are many applications of deep learning (it’s not only image recognition!). I collected some introductory materials to cover its various aspects (beware: they are of various difficulty). Don’t try to read them all - I list them for inspiration, not intimidation! General The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy How convolutional neural networks see the world - Keras Blog What convolutional neural networks look at when they see nudity – Clarifai Blog (NSFW) Convolutional neural networks for artistic style transfer by Harish Nrayanan Dreams, Drugs and ConvNets - my slides (NSFW); I am considering turning it into a longer post on machine learning vs human learning, based on common mistakes Technical Yes you should understand backpropby Andrej Karpathy Transfer Learning using Keras by Prakash Vanapalli Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch) Minimal and Clean Reinforcement Learning Examples An overview of gradient descent optimization algorithms by Sebastian Ruder Picking an optimizer for Style Transfer by Slav Ivanov Building Autoencoders in Keras by Francois Chollet Understanding LSTM Networks by Chris Olah Recurrent Neural Networks & LSTMs by Rohan Kapur Oxford Deep NLP 2017 course List of resources How to Start Learning Deep Learning by Ofir Press A Guide to Deep Learning by YN^2 Staying up-to-date: r/MachineLearning Reddit channel covering most of new stuff distill.pub - an interactive, visual, open-access journal for machine learning research, with expository articles my links at pinboard.in/u:pmigdal/t:deep-learning - though, just saving, not an automatic recommendation @fastml_extra Twitter channel GitXiv for papers with code don’t be afraid to read academic papers; some are well-written and insightful (if you own Kindle or another e-reader, I recommend Dontprint) Data (usually from challenges) Kaggle AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017 http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

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iNaturalist 2017 Competition (675k images with 5k species), vide Mushroom AI

Thanks I would like to thank Kasia Kulma, Martina Pugliese, Paweł Subko, Monika Pawłowska and Łukasz Kidziński for helpful feedback on the content and to Sarah Martin for polishing my English. If you recommend a source that helped you with your adventure with deep learning - feel invited to contact me! (@pmigdal for short links, an email for longer remarks.) The deep learning meme is not mine - I’ve just I rewrote from Theano to Keras (with TensorFlow backend). 1. NOAA Right Whale Recognition, Winners’ Interview (1st place, Jan 2016), and a fresh one: Deep learning for satellite imagery via image segmentation (4th place, Apr 2017). ↩ 2. This January during a 5-day workshop 6 high-school students participated in a rather NSFL project - constructing a neural network for detecting trypophobia triggers, see e.g. grzegorz225/trypophobia-detector and cytadela8/trypophobia_detector. ↩ 3. It made a few episodes of webcomics obsolete: xkcd: Tasks (totally, by Park or Bird?), xkcd: Game AI (partially, by AlphaGo), PHD Comics: If TV Science was more like REAL Science (not exactly, but still it’s cool, by LapSRN). ↩ 4. The title alludes to The Unreasonable Effectiveness of Mathematics in the Natural Sciences by Eugene Wigner (1960), one of my favourite texts in philosophy of science. Along with More is Different by PW Andreson (1972) and Genesis and development of a scientific fact (pdf here) by Ludwik Fleck (1935). ↩ 5. If your background is in quantum information, the only thing you need to change is ℂ to ℝ. Just expect less tensor structure, but more convolutions. ↩ 6. Is it only me, or doesTheano tensor dimension order sound like some secret convent? Before you start searching how to join it: it is about the shape of multi-dimensional arrays: (samples, channels, x, y) rather than TensorFlow’s (samples, x, y, channels) . ↩

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Piotr Migdał - a data science freelancer, with PhD in quantum physics; based in Warsaw, Poland. Believing in side projects, active in gifted developing Quantum Gameeducation, and working as a datathe science instructor at deepsense.io. Python (+ ε of R) for data analysis and machine learning, JavaScript for data visualization. Currently focusing on deep learning.


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Learning Deep Learning With Keras

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