diff --git a/tutorials/60-minute-blitz/60-minute-blitz.jl b/tutorials/60-minute-blitz/60-minute-blitz.jl index d0583bd3..b8eea0b9 100644 --- a/tutorials/60-minute-blitz/60-minute-blitz.jl +++ b/tutorials/60-minute-blitz/60-minute-blitz.jl @@ -1,447 +1,2914 @@ -# Deep Learning with Flux: A 60 Minute Blitz -# ===================== - -# This is a quick intro to [Flux](https://github.com/FluxML/Flux.jl) loosely -# based on [PyTorch's -# tutorial](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html). -# It introduces basic Julia programming, as well Zygote, a source-to-source -# automatic differentiation (AD) framework in Julia. -# We'll use these tools to build a very simple neural network. - -# Arrays -# ------- - -# The starting point for all of our models is the `Array` (sometimes referred to -# as a `Tensor` in other frameworks). This is really just a list of numbers, -# which might be arranged into a shape like a square. Let's write down an array -# with three elements. +### A Pluto.jl notebook ### +# v0.19.27 + +using Markdown +using InteractiveUtils + +# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error). +macro bind(def, element) + quote + local iv = try + Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value + catch + b -> missing + end + local el = $(esc(element)) + global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el) + el + end +end -x = [1, 2, 3] +# ╔═╡ 5705eee1-f4a2-472e-a0f4-e66310439943 +using Flux -# Here's a matrix – a square array with four elements. +# ╔═╡ e5dd2937-da79-4ad8-8d24-05d587faf39f +begin + using MLDatasets + cifar_train_data = MLDatasets.CIFAR10(:train)[:] + cifar_labels = Flux.onehotbatch(cifar_train_data.targets, 0:9) +end -x = [1 2; 3 4] +# ╔═╡ 9af7c473-81e4-41be-8974-7ea7f78b5bd9 +begin + import PlutoUI + PlutoUI.TableOfContents() +end -# We often work with arrays of thousands of elements, and don't usually write -# them down by hand. Here's how we can create an array of 5×3 = 15 elements, -# each a random number from zero to one. +# ╔═╡ e64069e4-57d9-11ee-0546-8bdf6341f644 +md"# Deep Learning with Flux: A 60 Minute Blitz" -x = rand(5, 3) +# ╔═╡ e8f659c9-bad5-45d3-8e84-2961d8c3195f +md"This is a quick intro to [Flux](https://github.com/FluxML/Flux.jl) loosely +based on [PyTorch's +tutorial](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html). +It introduces basic Julia programming, as well Zygote, a source-to-source +automatic differentiation (AD) framework in Julia. +We'll use these tools to build a very simple neural network." -# There's a few functions like this; try replacing `rand` with `ones`, `zeros`, -# or `randn` to see what they do. +# ╔═╡ a554a8fc-4977-41f9-8e2f-d0dbf583bb2b +md"## Arrays" -# By default, Julia works stores numbers is a high-precision format called -# `Float64`. In ML we often don't need all those digits, and can ask Julia to -# work with `Float32` instead. We can even ask for more digits using `BigFloat`. +# ╔═╡ 3901f51b-b9a1-423b-ad49-fec5698bc3bb +md"The starting point for all of our models is the `Array` (sometimes referred to +as a `Tensor` in other frameworks). This is really just a list of numbers, +which might be arranged into a shape like a square. Let's write down an array +with three elements." -x = rand(BigFloat, 5, 3) -#- -x = rand(Float32, 5, 3) +# ╔═╡ deac07e8-53f6-41e3-9ceb-bc6a120c9ae3 +[1, 2, 3] -# We can ask the array how many elements it has. +# ╔═╡ d919ddd2-1a43-4d4c-bd9a-ebc00bfbf8c7 +md"Here's a matrix – a square array with four elements." -length(x) +# ╔═╡ 0ed777d0-9f54-45dc-8505-f90d5a0ce95e +[1 2; 3 4] -# Or, more specifically, what size it has. +# ╔═╡ c7c76494-f6e1-4fb6-8d5f-356de1dabbc9 +md"We often work with arrays of thousands of elements, and don't usually write +them down by hand. Here's how we can create an array of 5×3 = 15 elements, +each a random number from zero to one." -size(x) +# ╔═╡ b48ff698-4c87-4f8f-81b7-d4f659e67ef8 +rand(5, 3) -# We sometimes want to see some elements of the array on their own. +# ╔═╡ 36c3e54b-7e39-4983-8a23-1d1ae50bf312 +md"There's a few functions like this; try replacing `rand` with `ones`, `zeros`, +or `randn` to see what they do. -x -#- -x[2, 3] +By default, Julia works stores numbers is a high-precision format called +`Float64`. In ML we often don't need all those digits, and can ask Julia to +work with `Float32` instead. We can even ask for more digits using `BigFloat`." -# This means get the second row and the third column. We can also get every row -# of the third column. +# ╔═╡ 1e0eadb7-458f-4929-986b-33678447f7fe +rand(BigFloat, 5, 3) -x[:, 3] +# ╔═╡ c9ca7be3-f490-4578-83af-64101694fdfe +m = rand(Float32, 5, 3) -# We can add arrays, and subtract them, which adds or subtracts each element of -# the array. +# ╔═╡ 8c4a0a76-e23d-4786-9519-61fb7cf74af3 +md"We can ask the array how many elements it has." -x + x -#- -x - x +# ╔═╡ 9a968107-4e6b-441c-8622-4247c63e6cb5 +length(m) -# Julia supports a feature called *broadcasting*, using the `.` syntax. This -# tiles small arrays (or single numbers) to fill bigger ones. +# ╔═╡ 7c315291-9642-4306-813d-18b7b59b65ac +md"Or, more specifically, what size it has." -x .+ 1 +# ╔═╡ f076b675-4d7f-4dc4-a00c-c31067899ec2 +size(m) -# We can see Julia tile the column vector `1:5` across all rows of the larger -# array. +# ╔═╡ 5f6730cb-ca2c-454f-b7e8-064c4fb1480d +md"We sometimes want to see some elements of the array on their own." -zeros(5,5) .+ (1:5) +# ╔═╡ 30a45ace-796a-4f51-8889-8563c48a7602 +m[2, 3] -# The x' syntax is used to transpose a column `1:5` into an equivalent row, and -# Julia will tile that across columns. +# ╔═╡ 651164ab-f4ed-4999-acdd-9afa253ec178 +md"This means get the second row and the third column. We can also get every row of +the third column." -zeros(5,5) .+ (1:5)' +# ╔═╡ 6f942d26-371c-4f69-b014-e7d5959ea804 +m[:, 3] -# We can use this to make a times table. +# ╔═╡ 328cb705-fb37-4af8-a678-b8d82f7695cc +md"We can add arrays, and subtract them, which adds or subtracts each element of +the array." -(1:5) .* (1:5)' +# ╔═╡ 25465742-276f-4fd7-a956-5e1192415a3b +m + m -# Finally, and importantly for machine learning, we can conveniently do things like -# matrix multiply. +# ╔═╡ 1760b20c-3206-4f18-9f39-2a0c8c111118 +m - m -W = randn(5, 10) -x = rand(10) -W * x +# ╔═╡ 722c88db-9f9b-4a2e-b760-6c957b3112f3 +md"Julia supports a feature called *broadcasting*, using the `.` syntax. This +tiles small arrays (or single numbers) to fill bigger ones." -# Julia's arrays are very powerful, and you can learn more about what they can -# do [here](https://docs.julialang.org/en/v1/manual/arrays/). +# ╔═╡ 7d3bb79b-ad4a-4752-b1b7-244d6e81f3d8 +m .+ 1 -# ### CUDA Arrays +# ╔═╡ 216b300d-9514-46f8-8471-d46864c7763d +md"We can see Julia tile the column vector `1:5` across all rows of the larger +array." -# CUDA functionality is provided separately by the [CUDA -# package](https://github.com/JuliaGPU/CUDA.jl). If you have a GPU and CUDA -# available, you can run `] add CUDA` in a REPL or IJulia to get it. +# ╔═╡ 1d46dace-253d-4bdc-9a1e-36c11ccd5119 +zeros(5, 5) .+ (1:5) -# Once CUDA is loaded you can move any array to the GPU with the `cu` -# function, and it supports all of the above operations with the same syntax. +# ╔═╡ 502de11b-ce00-4f78-8b31-42a99ffcc874 +md"The x' syntax is used to transpose a column `1:5` into an equivalent row, and +Julia will tile that across columns." -## using CUDA -## x = cu(rand(5, 3)) +# ╔═╡ 4b2cebee-ee12-4b9e-ba77-c5479cc47600 +zeros(5, 5) .+ (1:5)' -# Automatic Differentiation -# ------------------------- +# ╔═╡ 30f6c841-d7a3-4bbd-aefc-52a984ca7fe2 +md"We can use this to make a times table." -# You probably learned to take derivatives in school. We start with a simple -# mathematical function like +# ╔═╡ 8c62c50e-5b47-497f-9bc9-6bd237921b08 +(1:5) .* (1:5)' -f(x) = 3x^2 + 2x + 1 +# ╔═╡ 60b64c89-e5ff-42a6-bec5-d9572ba62b61 +md"Finally, and importantly for machine learning, we can conveniently do things like +matrix multiply." -f(5) +# ╔═╡ 87b59a63-59cf-48cf-93c2-3ac29ce975f6 +begin + local W = randn(5, 10) + local x = rand(10) + W * x +end -# In simple cases it's pretty easy to work out the gradient by hand – here it's -# `6x+2`. But it's much easier to make Flux do the work for us! +# ╔═╡ e7e46f75-acd2-4752-b6de-ba23f1df6c72 +md"Julia's arrays are very powerful, and you can learn more about what they can +do [here](https://docs.julialang.org/en/v1/manual/arrays/). -using Flux: gradient +### GPU support -df(x) = gradient(f, x)[1] +Using GPU instead of CPU can vastly improves computation speed. Please refer to +[Flux GPU Support](https://fluxml.ai/Flux.jl/stable/gpu/) page which has detailed +information on how to set things up with various GPU vendors. +" -df(5) +# ╔═╡ 10956af3-b8ec-4ac5-b8b2-23b9f427b7c9 +md"## Automatic Differentiation" -# You can try this with a few different inputs to make sure it's really the same -# as `6x+2`. We can even do this multiple times (but the second derivative is a -# fairly boring `6`). +# ╔═╡ 01a5fe17-8cfd-41d8-8009-8eeeb0f9feb0 +md"### Basics" -ddf(x) = gradient(df, x)[1] +# ╔═╡ 91b9e9b2-b226-4a00-988b-cced866135d2 +md"You probably learned to take derivatives in school. We start with a simple +mathematical function like" -ddf(5) +# ╔═╡ 5ea7b391-08cd-4dff-9224-c80b9c74d488 +begin + f(x) = 3x^2 + 2x + 1 + f(5) +end -# Flux's AD can handle any Julia code you throw at it, including loops, -# recursion and custom layers, so long as the mathematical functions you call -# are differentiable. For example, we can differentiate a Taylor approximation -# to the `sin` function. +# ╔═╡ a2b96d3a-a158-4ec4-9655-1f89b283450a +md"In simple cases it's pretty easy to work out the gradient by hand – here it's +`6x+2`. But it's much easier to make Flux do the work for us!" -mysin(x) = sum((-1)^k*x^(1+2k)/factorial(1+2k) for k in 0:5) +# ╔═╡ bf0e073b-bd24-480d-bc61-803cdeffdbfe +begin + df(x) = Flux.gradient(f, x)[1] + df(5) +end -x = 0.5 +# ╔═╡ de14eccd-d566-41f1-aba7-c2e9c683f93b +md"You can try this with a few different inputs to make sure it's really the same +as `6x+2`. We can even do this multiple times (but the second derivative is a +fairly boring `6`)." -mysin(x), gradient(mysin, x) -#- -sin(x), cos(x) +# ╔═╡ 1330e62b-aa96-4acd-8eef-ffae5371a17f +begin + ddf(x) = Flux.gradient(df, x)[1] + ddf(5) +end -# You can see that the derivative we calculated is very close to `cos(x)`, as we -# expect. +# ╔═╡ 4f7f6bb8-bf66-4c55-8aa7-32e5fc16dc1a +md"### Taylor approximation" -# This gets more interesting when we consider functions that take *arrays* as -# inputs, rather than just a single number. For example, here's a function that -# takes a matrix and two vectors (the definition itself is arbitrary) +# ╔═╡ 4fe5d397-46d2-44e9-96ea-4ff5ea55960e +md"Flux's AD can handle any Julia code you throw at it, including loops, +recursion and custom layers, so long as the mathematical functions you call +are differentiable. For example, we can differentiate a Taylor approximation +to the `sin` function." -myloss(W, b, x) = sum(W * x .+ b) +# ╔═╡ cca07c55-b1ad-42ba-8ce2-d4413ee8d219 +md"Move the slider below to recalculate `mysin` and its gradient! +You can see that the derivative we calculated is very close to `cos(x)`, as we +expect." -W = randn(3, 5) -b = zeros(3) -x = rand(5) +# ╔═╡ 091f47fb-2941-4403-98f0-8bbc924bbc7a +md"$(@bind z PlutoUI.Slider(-1.5:.25:2, default=.5))" -gradient(myloss, W, b, x) +# ╔═╡ 97fa7163-4dd0-4646-9e02-a131208bd4b3 +z -# Now we get gradients for each of the inputs `W`, `b` and `x`, which will come -# in handy when we want to train models. +# ╔═╡ 254b50d5-9ce9-4cbd-abf1-327ad4d6a1c7 +mysin(x) = sum((-1)^k * x^(1 + 2k) / factorial(1 + 2k) for k in 0:5) -# Because ML models can contain hundreds of parameters, Flux provides a slightly -# different way of writing `gradient`. We instead mark arrays with `param` to -# indicate that we want their derivatives. `W` and `b` represent the weight and -# bias respectively. +# ╔═╡ 6536589e-d243-45b7-b042-3732efddc63f +mysin(z), Flux.gradient(mysin, z) -using Flux: params +# ╔═╡ 20f79e0a-dfe0-441f-ba63-a4b9d2e4753d +sin(z), cos(z) -W = randn(3, 5) -b = zeros(3) -x = rand(5) +# ╔═╡ bacd05e1-7d24-424c-a0df-920a751f23e5 +md"### Arrays" -y(x) = sum(W * x .+ b) +# ╔═╡ 28d62259-9049-465f-a654-ec0358826c2b +md"This gets more interesting when we consider functions that take *arrays* as +inputs, rather than just a single number. For example, here's a function that +takes a matrix and two vectors (the definition itself is arbitrary)" -grads = gradient(()->y(x), params([W, b])) +# ╔═╡ 5105fc4b-d9e5-4c3f-9b16-e700a7be74bf +begin + local myloss(W, b, x) = sum(W * x .+ b) -grads[W], grads[b] + local W = randn(3, 5) + local b = zeros(3) + local x = rand(5) + Flux.gradient(myloss, W, b, x) +end -# We can now grab the gradients of `W` and `b` directly from those parameters. +# ╔═╡ 4f9c221b-d382-4467-b86f-ef2e3c51aebe +md"Now we get gradients for each of the inputs `W`, `b` and `x`, which will come +in handy when we want to train models. -# This comes in handy when working with *layers*. A layer is just a handy -# container for some parameters. For example, `Dense` does a linear transform -# for you. +Because ML models can contain hundreds of parameters, Flux provides a slightly +different way of writing `gradient`. We instead mark arrays with `param` to +indicate that we want their derivatives. `W` and `b` represent the weight and +bias respectively." -using Flux +# ╔═╡ a1c41e9a-1866-4654-b6a3-9321922db778 +md"### Flux.params" -m = Dense(10, 5) +# ╔═╡ 5a07e503-7f49-43ae-afb2-f9e5727f6b06 +begin + local W = randn(3, 5) + local b = zeros(3) + local x = rand(5) -# We can easily get the parameters of any layer or model with params with -# `params`. + local y(x) = sum(W * x .+ b) -params(m) + local grads = Flux.gradient(() -> y(x), Flux.params((W=W, b=b))) -# This makes it very easy to calculate the gradient for all -# parameters in a network, even if it has many parameters. -x = rand(Float32, 10) -m = Chain(Dense(10, 5, relu), Dense(5, 2), softmax) -l(x) = Flux.Losses.crossentropy(m(x), [0.5, 0.5]) -grads = gradient(params(m)) do - l(x) -end -for p in params(m) - println(grads[p]) + grads[W], grads[b] end +# ╔═╡ e51fe4ac-5ced-4f0a-bf20-e10864c931ce +md"We can now grab the gradients of `W` and `b` directly from those parameters if we use named tuples. -# You don't have to use layers, but they can be convenient for many simple kinds -# of models and fast iteration. +This comes in handy when working with *layers*. A layer is just a handy +container for some parameters. For example, `Dense` does a linear transform +for you." -# The next step is to update our weights and perform optimisation. As you might be -# familiar, *Gradient Descent* is a simple algorithm that takes the weights and steps -# using a learning rate and the gradients. `weights = weights - learning_rate * gradient` -# (note that `Flux.Optimise.update!(x, x̄)` already updates with the negative of x̄`). -using Flux.Optimise: update!, Descent -η = 0.1 -for p in params(m) - update!(p, η * grads[p]) -end +# ╔═╡ c76b9a22-ba54-42da-83ce-770713e001f1 +tiny_model = Flux.Dense(10, 5) -# While this is a valid way of updating our weights, it can get more complicated as the -# algorithms we use get more involved. +# ╔═╡ d5f387af-1a89-4e08-986a-81f5c4d7251c +md"We can easily get the parameters of any layer or model with +`params`:" -# Flux comes with a bunch of pre-defined optimisers and makes writing our own really simple. -# We just give it the learning rate η +# ╔═╡ 7546da12-5ba0-4e05-897e-b9ef918ea3ce +Flux.params(tiny_model) -opt = Descent(0.01) +# ╔═╡ 19546141-95e1-4e8d-8183-c7d0bd4eda2b +md"This makes it very easy to calculate the gradient for all +parameters in a network, even if it has many parameters." -# `Training` a network reduces down to iterating on a dataset multiple times, performing these -# steps in order. Just for a quick implementation, let’s train a network that learns to predict -# `0.5` for every input of 10 floats. `Flux` defines the `train!` function to do it for us. - -data, labels = rand(10, 100), fill(0.5, 2, 100) -loss(x, y) = Flux.Losses.crossentropy(m(x), y) -Flux.train!(loss, params(m), [(data,labels)], opt) -# You don't have to use `train!`. In cases where arbitrary logic might be better suited, -# you could open up this training loop like so: - -# ```julia -# for d in training_set # assuming d looks like (data, labels) -# # our super logic -# gs = gradient(params(m)) do #m is our model -# l = loss(d...) -# end -# update!(opt, params(m), gs) -# end -# ``` - -# Training a Classifier -# --------------------- - -# Getting a real classifier to work might help cement the workflow a bit more. -# [CIFAR10](url) is a dataset of 50k tiny training images split into 10 classes. - -# We will do the following steps in order: - -# * Load CIFAR10 training and test datasets -# * Define a Convolution Neural Network -# * Define a loss function -# * Train the network on the training data -# * Test the network on the test data - -# Loading the Dataset - -# [Metalhead.jl](https://github.com/FluxML/Metalhead.jl) is an excellent package -# that has a number of predefined and pretrained computer vision models. -# It also has a number of dataloaders that come in handy to load datasets. - -using Statistics -using Flux, Flux.Optimise -using MLDatasets: CIFAR10 -using Images.ImageCore -using Flux: onehotbatch, onecold -using Base.Iterators: partition -using CUDA - -# The image will give us an idea of what we are dealing with. -# ![title](https://pytorch.org/tutorials/_images/cifar10.png) - -train_x, train_y = CIFAR10(:train)[:] -labels = onehotbatch(train_y, 0:9) - -#The train_x contains 50000 images converted to 32 X 32 X 3 arrays with the third -# dimension being the 3 channels (R,G,B). Let's take a look at a random image from -# the train_x. For this, we need to permute the dimensions to 3 X 32 X 32 and use -# `colorview` to convert it back to an image. - -# Let's take a look at a random image from the dataset - -using Plots -image(x) = colorview(RGB, permutedims(x, (3, 2, 1))) -plot(image(train_x[:,:,:,rand(1:end)])) - - -# The images are simply 32 X 32 matrices of numbers in 3 channels (R,G,B). We can now -# arrange them in batches of say, 1000 and keep a validation set to track our progress. -# This process is called minibatch learning, which is a popular method of training -# large neural networks. Rather that sending the entire dataset at once, we break it -# down into smaller chunks (called minibatches) that are typically chosen at random, -# and train only on them. It is shown to help with escaping -# [saddle points](https://en.wikipedia.org/wiki/Saddle_point). - - -# The first 49k images (in batches of 1000) will be our training set, and the rest is -# for validation. `partition` handily breaks down the set we give it in consecutive parts -# (1000 in this case). - -train = ([(train_x[:,:,:,i], labels[:,i]) for i in partition(1:49000, 1000)]) |> gpu -valset = 49001:50000 -valX = train_x[:,:,:,valset] |> gpu -valY = labels[:, valset] |> gpu - -# ## Defining the Classifier -# -------------------------- -# Now we can define our Convolutional Neural Network (CNN). - -# A convolutional neural network is one which defines a kernel and slides it across a matrix -# to create an intermediate representation to extract features from. It creates higher order -# features as it goes into deeper layers, making it suitable for images, where the structure of -# the subject is what will help us determine which class it belongs to. - -m = Chain( - Conv((5,5), 3=>16, relu), - MaxPool((2,2)), - Conv((5,5), 16=>8, relu), - MaxPool((2,2)), - x -> reshape(x, :, size(x, 4)), - Dense(200, 120), - Dense(120, 84), - Dense(84, 10), - softmax) |> gpu - -#- -# We will use a crossentropy loss and the Momentum optimiser here. Crossentropy will be a -# good option when it comes to working with multiple independent classes. Momentum smooths out -# the noisy gradients and helps towards a smooth convergence. Gradually lowering the -# learning rate along with momentum helps to maintain a bit of adaptivity in our optimisation, -# preventing us from overshooting our desired destination. -#- - -using Flux: crossentropy, Momentum - -loss(x, y) = sum(crossentropy(m(x), y)) -opt = Momentum(0.01) +# ╔═╡ 7017a17d-cc25-47ad-ab97-a36724e48f6b +md"### Simple Trainer" -# We can start writing our train loop where we will keep track of some basic accuracy -# numbers about our model. We can define an `accuracy` function for it like so. +# ╔═╡ f1b11e5d-8cfc-4f42-9dc5-ff164a5a0242 +begin + local x = rand(Float32, 10) + model = Chain(Dense(10, 5, relu), Dense(5, 2), softmax) + loss(x) = Flux.Losses.crossentropy(model(x), [0.5, 0.5]) + grads = gradient(Flux.params(model)) do + loss(x) + end + for p in Flux.params(model) + println(grads[p]) + end +end -accuracy(x, y) = mean(onecold(m(x), 0:9) .== onecold(y, 0:9)) +# ╔═╡ 5bd758e0-62b6-422d-91b5-d55a8258d533 +md"You don't have to use layers, but they can be convenient for many simple kinds +of models and fast iteration. -# ## Training -# ----------- +The next step is to update our weights and perform optimisation. As you might be +familiar, *Gradient Descent* is a simple algorithm that takes the weights and steps +using a learning rate and the gradients. `weights = weights - learning_rate * gradient` +(note that `Flux.Optimise.update!(x, x̄)` already updates with the negative of `x̄`)." -# Training is where we do a bunch of the interesting operations we defined earlier, -# and see what our net is capable of. We will loop over the dataset 10 times and -# feed the inputs to the neural network and optimise. - -epochs = 10 - -for epoch = 1:epochs - for d in train - gs = gradient(params(m)) do - l = loss(d...) +# ╔═╡ 3c7a0bee-cc55-4ed4-9439-08ffcff0028f +begin + η = 0.1 # learining rate + for p in Flux.params(model) + Flux.update!(p, η * grads[p]) end - update!(opt, params(m), gs) - end - @show accuracy(valX, valY) end -# Seeing our training routine unfold gives us an idea of how the network learnt the -# This is not bad for a small hand-written network, trained for a limited time. +# ╔═╡ a9f167c9-0962-467b-9a75-6df12774fcaf +md"While this is a valid way of updating our weights, it can get more complicated as the +algorithms we use get more involved. -# Training on a GPU -# ----------------- +Flux comes with a bunch of pre-defined optimisers and makes writing our own really simple. +We just give it the learning rate η" -# The `gpu` functions you see sprinkled through this bit of the code tell Flux to move -# these entities to an available GPU, and subsequently train on it. No extra faffing -# about required! The same bit of code would work on any hardware with some small -# annotations like you saw here. +# ╔═╡ 4c8eebcb-9a08-4e02-855b-f797835fb072 +opt = Descent(0.01) -# ## Testing the Network -# ---------------------- +# ╔═╡ a80435e0-57c1-483a-b093-857d6e67d5a5 +md"`Training` a network reduces down to iterating on a dataset multiple times, performing these +steps in order. Just for a quick implementation, let’s train a network that learns to predict +`0.5` for every input of 10 floats. `Flux` defines the `train!` function to do it for us." + +# ╔═╡ e111fa78-5cd0-4086-a040-9dff31f83167 +begin + data, labels = rand(10, 100), fill(0.5, 2, 100) + loss(x, y) = Flux.Losses.crossentropy(model(x), y) + Flux.train!(loss, Flux.params(model), [(data, labels)], opt) + # XXX: Check Flux docs on not updating some array with train!, drop Flux.params +end -# We have trained the network for 10 passes over the training dataset. But we need to -# check if the network has learnt anything at all. +# ╔═╡ f8427698-68a6-416f-b02f-38e15f54c8d4 +md"You don't have to use `train!`. In cases where arbitrary logic might be better suited, +you could open up this training loop like so: + +```julia +for d in training_set # assuming d looks like (data, labels) + # our super logic + gs = gradient(params(m)) do #m is our model + l = loss(d...) + end + update!(opt, params(m), gs) +end +```" -# We will check this by predicting the class label that the neural network outputs, and -# checking it against the ground-truth. If the prediction is correct, we add the sample -# to the list of correct predictions. This will be done on a yet unseen section of data. +# ╔═╡ 7d1fe65b-d51e-4668-bbba-15aa1bb4abf1 +md"## Training Classifier" -# Okay, first step. Let us perform the exact same preprocessing on this set, as we did -# on our training set. +# ╔═╡ 62097f96-2900-4ce7-91ca-c2ab34971332 +md"Getting a real classifier to work might help cement the workflow a bit more. +[CIFAR10](https://juliaml.github.io/MLDatasets.jl/stable/datasets/vision/#MLDatasets.CIFAR10) is a dataset of 50k tiny training images split into 10 classes." -test_x, test_y = CIFAR10(:test)[:] -test_labels = onehotbatch(test_y, 0:9) +# ╔═╡ 7adeb72a-088d-4c88-ac4f-e6ba1e512ecb +begin + import FileIO: load + # Images.jl contains a `show` method for PNG images + import Images: Images + load(download("https://pytorch.org/tutorials/_images/cifar10.png")) +end -test = gpu.([(test_x[:,:,:,i], test_labels[:,i]) for i in partition(1:10000, 1000)]) +# ╔═╡ 33c598fe-0d50-44b2-808a-59db6a8029e8 +md"We will do the following steps in order: -# Next, display an image from the test set. +* Load CIFAR10 training and test datasets +* Define a Convolution Neural Network +* Define a loss function +* Train the network on the training data +* Test the network on the test data -plot(image(test_x[:,:,:,rand(1:end)])) +### Loading the Dataset -# The outputs are energies for the 10 classes. Higher the energy for a class, the more the -# network thinks that the image is of the particular class. Every column corresponds to the -# output of one image, with the 10 floats in the column being the energies. +[Metalhead.jl](https://github.com/FluxML/Metalhead.jl) is an excellent package +that has a number of predefined and pretrained computer vision models. +It also has a number of dataloaders that come in handy to load datasets." -# Let's see how the model fared. +# ╔═╡ 809878f0-8513-410c-95a9-6b2aa18c5dac +md"The `train_x` contains 50000 images converted to $32 \times 32 \times 3$ +arrays with the third dimension being the 3 channels (R,G,B). +Let's take a look at a random image from +the `train_x`. For this, we need to permute the dimensions to +$3 \times 32 \times 32$ and use +`colorview` to convert it back to an image. -ids = rand(1:10000, 5) -rand_test = test_x[:,:,:,ids] |> gpu -rand_truth = test_y[ids] -m(rand_test) +Let's take a look at a random image from the dataset:" -# This looks similar to how we would expect the results to be. At this point, it's a good -# idea to see how our net actually performs on new data, that we have prepared. +# ╔═╡ 2341928c-b409-4963-9e3d-d8edfd3f86e1 +begin + imgsize = 128 + md"Resize image + $(@bind imgsize PlutoUI.Slider(64:64:1024, default=128))" +end -accuracy(test[1]...) +# ╔═╡ 1d557716-d5b1-4b9f-a39e-7c2b7156afd4 +begin + using Plots + image(x) = Images.colorview(RGB, permutedims(x, (3, 2, 1))) + plot(image(cifar_train_data.features[:, :, :, rand(1:end)]), size=(imgsize, imgsize)) +end -# This is much better than random chance set at 10% (since we only have 10 classes), and -# not bad at all for a small hand written network like ours. +# ╔═╡ 1e3bd6c8-8625-4bc3-87b8-004fafa9104f +md"The images are simply $32 \times 32$ matrices of numbers in 3 channels (R,G,B). We can now +arrange them in batches of say, 1000 and keep a validation set to track our progress. +This process is called minibatch learning, which is a popular method of training +large neural networks. Rather that sending the entire dataset at once, we break it +down into smaller chunks (called minibatches) that are typically chosen at random, +and train only on them. It is shown to help with escaping +[saddle points](https://en.wikipedia.org/wiki/Saddle_point)." + +# ╔═╡ 6c2558bd-8c42-40ea-a465-e1ae6597546a +md"The images in `MLDatasets` are separated to training (50k) and testing (10k) +sets. `partition` handily breaks down the set we give it in consecutive parts +(1000 in this case)." + +# ╔═╡ c8d3b94c-045b-4a7a-a970-728d43ed1b40 +cifar_train = ([(cifar_train_data.features[:, :, :, i], cifar_labels[:, i]) for i in Iterators.partition(1:50000, 1000)]) |> gpu + +# ╔═╡ ffd6d306-a771-4fe0-9bab-57e981bb4c5d +cifar_test = MLDatasets.CIFAR10(:test)[:] |> gpu + +# ╔═╡ 98837594-4421-43c6-9d70-256dbd126318 +md"### Defining the Classifier +Now we can define our Convolutional Neural Network (CNN). + +A convolutional neural network is one which defines a kernel and slides it across a matrix +to create an intermediate representation to extract features from. It creates higher order +features as it goes into deeper layers, making it suitable for images, where the structure of +the subject is what will help us determine which class it belongs to." + +# ╔═╡ f05f1469-3b91-434e-929c-17a030ae0565 +cifar_model = Chain( + Conv((5, 5), 3 => 16, relu), + MaxPool((2, 2)), + Conv((5, 5), 16 => 8, relu), + MaxPool((2, 2)), + x -> reshape(x, :, size(x, 4)), + Dense(200, 120), + Dense(120, 84), + Dense(84, 10), + softmax) |> gpu + +# ╔═╡ f5211ac3-354e-446f-aa0a-1955f0e57c8d +begin + using OneHotArrays, Statistics + cifar_accuracy(x, y) = mean(onecold(cifar_model(x), 0:9) .== y) +end -# Let's take a look at how the net performed on all the classes performed individually. +# ╔═╡ ee50542a-a0c1-48f9-be5e-40453576b9ba +md"We will use a `crossentropy` loss and the `Momentum` optimiser here. Crossentropy will be a +good option when it comes to working with multiple independent classes. Momentum smooths out +the noisy gradients and helps towards a smooth convergence. Gradually lowering the +learning rate along with momentum helps to maintain a bit of adaptivity in our optimisation, +preventing us from overshooting our desired destination." + +# ╔═╡ 9a19e9bc-5fd4-4d88-a125-b963321d9e9b +begin + cifar_loss(x, y) = sum(Flux.Losses.crossentropy(cifar_model(x), y)) + cifar_opt = Momentum(0.03) +end -class_correct = zeros(10) -class_total = zeros(10) -for i in 1:10 - preds = m(test[i][1]) - lab = test[i][2] - for j = 1:1000 - pred_class = findmax(preds[:, j])[2] - actual_class = findmax(lab[:, j])[2] - if pred_class == actual_class - class_correct[pred_class] += 1 +# ╔═╡ 18e17600-b2b3-4fd3-a03d-bd4c570db962 +md"We can start writing our train loop where we will keep track of some basic accuracy +numbers about our model. We can define an `accuracy` function for it like so." + +# ╔═╡ ba2e433e-f0a0-45e8-90b1-eb4e3bd21068 +md"### Training + +Training is where we do a bunch of the interesting operations we defined earlier, +and see what our net is capable of. We will loop over the dataset 10 times and +feed the inputs to the neural network and optimise." + +# ╔═╡ eec97769-b4b4-4aa4-b12e-0bb215d58fcc +begin + epochs = 10 + + for epoch = 1:epochs + for d in cifar_train + gs = gradient(Flux.params(cifar_model)) do + l = cifar_loss(d...) + end + Flux.update!(opt, Flux.params(cifar_model), gs) + end + @info "accuracy: $(cifar_accuracy(cifar_test.features, cifar_test.targets))" end - class_total[actual_class] += 1 - end end -class_correct ./ class_total - -# The spread seems pretty good, with certain classes performing significantly better than the others. -# Why should that be? +# ╔═╡ 00000000-0000-0000-0000-000000000001 +PLUTO_PROJECT_TOML_CONTENTS = """ +[deps] +FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0" +MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" +OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f" +Plots = 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╟─00000000-0000-0000-0000-000000000002 diff --git a/tutorials/60-minute-blitz/Manifest.toml b/tutorials/60-minute-blitz/Manifest.toml deleted file mode 100644 index 05e4161d..00000000 --- a/tutorials/60-minute-blitz/Manifest.toml +++ /dev/null @@ -1,660 +0,0 @@ -# This file is machine-generated - editing it directly is not advised - -[[AbstractFFTs]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "051c95d6836228d120f5f4b984dd5aba1624f716" -uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "0.5.0" - -[[AbstractTrees]] -deps = ["Markdown"] -git-tree-sha1 = "33e450545eaf7699da1a6e755f9ea65f14077a45" -uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" -version = "0.3.3" - -[[Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "4146c39f29be88c3f0cef732f86e5ab640d2e22d" -uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.1.1" - -[[Artifacts]] -deps = ["Pkg"] -git-tree-sha1 = "c30985d8821e0cd73870b17b0ed0ce6dc44cb744" -uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" -version = "1.3.0" - 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