### A Pluto.jl notebook ###
# v0.16.1

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 el = $(esc(element))
        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
        el
    end
end

# ╔═╡ b02f39a2-2b91-11ec-0131-3d5c8629c0a1
begin
	using Flux
	using Flux: onehotbatch, onecold
	using Flux.Data: DataLoader
	using Flux.Optimise: Optimiser, WeightDecay
	using Flux.Losses: logitcrossentropy
	
	using Statistics, Random, CUDA, PlutoUI
	using ProgressMeter: @showprogress
	using Images: Gray
	import Dates
	import MLDatasets
	import BSON
end

# ╔═╡ cc864502-6eec-4c92-ace8-f7d72fd66e8f
md"# Digit recognition with a Convolutional Neural Network"

# ╔═╡ c74eb080-1d62-4d10-b596-d593dc580145
md"Convenience struct ```Args``` to hold all our arguments:"

# ╔═╡ b9f67b96-6896-45db-9d1b-79ca6112612a
Base.@kwdef mutable struct Args
	η = 3e-4 			# Learning rate
	λ = 0 				# L2 regularizer param, implemented at weight decay
	batchsize = 128
	epochs = 10
	seed = 0
	use_cuda = true
	return_model = true
	infotime = 1
	checktime = 5
	tblogger = true
	savepath = "runs/$(Dates.today())/"
end

# ╔═╡ 9cf6e113-e799-4a4e-92e3-84e51eb8e74f
md"We'll be using the [LeNet5](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf) architecture:"

# ╔═╡ 8fee236e-c453-489e-b792-9e684b1d1141
function LeNet5(; imgsize=(28,28,1), nclasses=10)
	out_conv_size = (imgsize[1]÷4-3, imgsize[2]÷4-3, 16)
	
	Chain(
		# First convolutional iteration
		Conv((5,5), imgsize[end]=>6, relu),
		MaxPool((2,2)),
		# Second convolutional iteration
		Conv((5,5), 6=>16, relu),
		MaxPool((2,2)),
		flatten,
		Dense(prod(out_conv_size), 120, relu),
		Dense(120, 84, relu),
		Dense(84, nclasses)
		)
end

# ╔═╡ 6870a7f2-2bef-405d-a31d-89bb05ed18cf
md"## Download & prepare data"

# ╔═╡ 824347c4-1cf8-4794-8821-c07ebb5a2892
function load_data(args)
	# Downloading from MLDatasets
	ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
	
	xtrain, ytrain = MLDatasets.MNIST.traindata(Float32)
	xtest, ytest = MLDatasets.MNIST.testdata(Float32)
	
	# Reshaping images to have a color channel
	xtrain′ = reshape(xtrain, 28, 28, 1, :)
	xtest′ = reshape(xtest, 28, 28, 1, :)
	
	# Saving labels as onehotbatches
	ytrain′ = onehotbatch(ytrain, sort(unique(ytrain)))
	ytest′ = onehotbatch(ytest, sort(unique(ytest)))
	
	# Getting data loaders
	train_loader = DataLoader((xtrain′, ytrain′), 
		batchsize=args.batchsize, shuffle=true)
	test_loader = DataLoader((xtest′, ytest′), batchsize=args.batchsize)
	
	train_loader, test_loader
end

# ╔═╡ f0720c68-3582-4c23-885b-80937693b5a3
md"## Loss & accuracy functions"

# ╔═╡ ed63f032-6d9d-48e3-a2cf-b22f2c77f6b2
loss(ŷ, y) = logitcrossentropy(ŷ, y)

# ╔═╡ 9a5f495f-ef25-44ed-8364-ef06ad49e461
function eval_loss_accuracy(loader, model, device)
	ltot = Float32(0)
	acc = Float32(0)
	ntot = UInt64(0)
	for (x′, y′) ∈ loader
		x, y = device(x′), device(y′)
		ŷ = model(x)
		ltot += loss(ŷ, y) * size(x)[end]
		acc += sum(onecold(ŷ |> cpu) .== onecold(y |> cpu))
		ntot += size(x)[end]
	end
	loss_val = round(ltot/ntot, digits=4)
	acc_val = round(100acc/ntot, digits=4)
	
	loss_val, acc_val
end

# ╔═╡ bfed0f44-0f53-4344-8280-cebeebfd59b2
md"## Training the model"

# ╔═╡ fcb5b8e2-e713-4f39-ae02-5db8e8f70454
function train(; kws...)
	args = Args(; kws...)
	args.seed > 0 && Random.seed!(args.seed)
	use_cuda = args.use_cuda && CUDA.functional()
	# Use GPU via CUDA if available
	if use_cuda
		@info "Training on GPU"
		device = gpu
	else
		@info "Training on CPU"
		device = cpu
	end
	
	trainloader, testloader = load_data(args)
	@info "MNIST: $(trainloader.nobs) train and $(testloader.nobs) test examples"
	
	model = device(LeNet5())
	@info "LeNet5 model: $(sum(length, params(model))) trainable params"
	
	parameters = params(model)
	
	# Optimizer settings
	if args.λ > 0
		optimizer = Optimiser(WeightDecay(args.λ), ADAM(args.η))
	else
		optimizer = ADAM(args.η)
	end
	
	# periodic reporting function
	function report(epoch)
		train = eval_loss_accuracy(trainloader, model, device)
		test = eval_loss_accuracy(testloader, model, device)
		@info "Epoch $epoch:\n\tTrain $train,\n\tTest $test"
	end
	
	# Training
	@info "Training started"
	report(0)
	for epoch ∈ 1:args.epochs
		@showprogress for (x, y) ∈ trainloader
			x, y = device(x), device(y)
			grads = Flux.gradient(parameters) do
				ŷ = model(x)
				loss(ŷ, y)
			end
			Flux.Optimise.update!(optimizer, parameters, grads)
		end
		
		# Logging
		epoch % args.infotime == 0 && report(epoch)
		if args.checktime > 0 && epoch % args.checktime == 0
			!ispath(args.savepath) && mkpath(args.savepath)
			modelpath = joinpath(args.savepath, "model-epoch$(epoch).bson")
			let model = cpu(model) # return model to CPU before serialization
				BSON.@save modelpath model
			end
			@info "Model saved in $modelpath."
		end
	end
	args.return_model && return cpu(model)
end

# ╔═╡ 8a8c6d53-8542-4755-9074-04c22f2171d2
md"## Using the model"

# ╔═╡ 8143d560-60d2-41e4-a6da-f4480fc3d879
model = train()

# ╔═╡ 251678dc-5e44-444f-946e-7e16b2f30ed0
md"Let's check it out:"

# ╔═╡ ac490092-cd19-417e-9e77-6935b8feb6eb
@bind N Slider(1:10000, default=20)

# ╔═╡ bc7caa57-2adb-4270-9814-bdefadc082bf
let (x, y) = MLDatasets.MNIST.testdata(Float32)
	x′ = reshape(x, 28, 28, 1, :)
	onecold(model(x′[:,:,:,N:N]), 0:9), Gray.(reverse(rotr90(x[:,:,N]), dims=2))
end

# ╔═╡ 51dfb587-d083-4986-bcde-74bbf32ad3ae
md"## Loading a trained model

Note that the ```@__MODULE__``` argument lets BSON use your current environment variables (it needs this because Flux uses NNlib)."

# ╔═╡ e7934634-7d6e-4e20-9575-e46fb7749786
model_load = BSON.load("runs/$(Dates.today())/model-epoch10.bson", @__MODULE__)

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[compat]
BSON = "~0.3.4"
CUDA = "~3.5.0"
Flux = "~0.12.7"
Images = "~0.24.1"
MLDatasets = "~0.5.11"
PlutoUI = "~0.7.16"
ProgressMeter = "~1.7.1"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
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[[ForwardDiff]]
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[[GPUArrays]]
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[[GPUCompiler]]
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[[GZip]]
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[[ImageMagick]]
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[[ImageMetadata]]
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[[ImageQualityIndexes]]
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[[ImageShow]]
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[[Images]]
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[[Imath_jll]]
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[[IniFile]]
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[[IntelOpenMP_jll]]
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[[Interpolations]]
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[[IntervalSets]]
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[[IrrationalConstants]]
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[[JLLWrappers]]
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[[JSON]]
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[[JpegTurbo_jll]]
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[[Juno]]
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[[LLVM]]
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[[LLVMExtra_jll]]
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[[LibCURL]]
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[[LibGit2]]
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[[Libtiff_jll]]
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[[LogExpFunctions]]
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[[Lz4_jll]]
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[[MAT]]
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[[MKL_jll]]
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[[MLDatasets]]
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[[MbedTLS]]
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[[Media]]
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[[Missings]]
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[[MozillaCACerts_jll]]
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[[NNlib]]
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[[NNlibCUDA]]
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[[Netpbm]]
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[[NetworkOptions]]
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[[OpenSSL_jll]]
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[[OpenSpecFun_jll]]
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[[OrderedCollections]]
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[[PNGFiles]]
deps = ["Base64", "CEnum", "ImageCore", "IndirectArrays", "OffsetArrays", "libpng_jll"]
git-tree-sha1 = "85e3436b18980e47604dd0909e37e2f066f54398"
uuid = "f57f5aa1-a3ce-4bc8-8ab9-96f992907883"
version = "0.3.10"

[[PaddedViews]]
deps = ["OffsetArrays"]
git-tree-sha1 = "646eed6f6a5d8df6708f15ea7e02a7a2c4fe4800"
uuid = "5432bcbf-9aad-5242-b902-cca2824c8663"
version = "0.5.10"

[[Parameters]]
deps = ["OrderedCollections", "UnPack"]
git-tree-sha1 = "34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe"
uuid = "d96e819e-fc66-5662-9728-84c9c7592b0a"
version = "0.12.3"

[[Parsers]]
deps = ["Dates"]
git-tree-sha1 = "a8709b968a1ea6abc2dc1967cb1db6ac9a00dfb6"
uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
version = "2.0.5"

[[Pkg]]
deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"

[[PkgVersion]]
deps = ["Pkg"]
git-tree-sha1 = "a7a7e1a88853564e551e4eba8650f8c38df79b37"
uuid = "eebad327-c553-4316-9ea0-9fa01ccd7688"
version = "0.1.1"

[[PlutoUI]]
deps = ["Base64", "Dates", "Hyperscript", "HypertextLiteral", "IOCapture", "InteractiveUtils", "JSON", "Logging", "Markdown", "Random", "Reexport", "UUIDs"]
git-tree-sha1 = "4c8a7d080daca18545c56f1cac28710c362478f3"
uuid = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
version = "0.7.16"

[[Preferences]]
deps = ["TOML"]
git-tree-sha1 = "00cfd92944ca9c760982747e9a1d0d5d86ab1e5a"
uuid = "21216c6a-2e73-6563-6e65-726566657250"
version = "1.2.2"

[[Printf]]
deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"

[[Profile]]
deps = ["Printf"]
uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"

[[ProgressMeter]]
deps = ["Distributed", "Printf"]
git-tree-sha1 = "afadeba63d90ff223a6a48d2009434ecee2ec9e8"
uuid = "92933f4c-e287-5a05-a399-4b506db050ca"
version = "1.7.1"

[[PyCall]]
deps = ["Conda", "Dates", "Libdl", "LinearAlgebra", "MacroTools", "Serialization", "VersionParsing"]
git-tree-sha1 = "169bb8ea6b1b143c5cf57df6d34d022a7b60c6db"
uuid = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
version = "1.92.3"

[[REPL]]
deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"

[[Random]]
deps = ["Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

[[Random123]]
deps = ["Libdl", "Random", "RandomNumbers"]
git-tree-sha1 = "0e8b146557ad1c6deb1367655e052276690e71a3"
uuid = "74087812-796a-5b5d-8853-05524746bad3"
version = "1.4.2"

[[RandomNumbers]]
deps = ["Random", "Requires"]
git-tree-sha1 = "043da614cc7e95c703498a491e2c21f58a2b8111"
uuid = "e6cf234a-135c-5ec9-84dd-332b85af5143"
version = "1.5.3"

[[RangeArrays]]
git-tree-sha1 = "b9039e93773ddcfc828f12aadf7115b4b4d225f5"
uuid = "b3c3ace0-ae52-54e7-9d0b-2c1406fd6b9d"
version = "0.3.2"

[[Ratios]]
deps = ["Requires"]
git-tree-sha1 = "01d341f502250e81f6fec0afe662aa861392a3aa"
uuid = "c84ed2f1-dad5-54f0-aa8e-dbefe2724439"
version = "0.4.2"

[[Reexport]]
git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
uuid = "189a3867-3050-52da-a836-e630ba90ab69"
version = "1.2.2"

[[Requires]]
deps = ["UUIDs"]
git-tree-sha1 = "4036a3bd08ac7e968e27c203d45f5fff15020621"
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
version = "1.1.3"

[[Rotations]]
deps = ["LinearAlgebra", "StaticArrays", "Statistics"]
git-tree-sha1 = "2ed8d8a16d703f900168822d83699b8c3c1a5cd8"
uuid = "6038ab10-8711-5258-84ad-4b1120ba62dc"
version = "1.0.2"

[[SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"

[[Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"

[[SharedArrays]]
deps = ["Distributed", "Mmap", "Random", "Serialization"]
uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"

[[SimpleTraits]]
deps = ["InteractiveUtils", "MacroTools"]
git-tree-sha1 = "5d7e3f4e11935503d3ecaf7186eac40602e7d231"
uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
version = "0.9.4"

[[Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"

[[SortingAlgorithms]]
deps = ["DataStructures"]
git-tree-sha1 = "b3363d7460f7d098ca0912c69b082f75625d7508"
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
version = "1.0.1"

[[SparseArrays]]
deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

[[SpecialFunctions]]
deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
git-tree-sha1 = "793793f1df98e3d7d554b65a107e9c9a6399a6ed"
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
version = "1.7.0"

[[StackViews]]
deps = ["OffsetArrays"]
git-tree-sha1 = "46e589465204cd0c08b4bd97385e4fa79a0c770c"
uuid = "cae243ae-269e-4f55-b966-ac2d0dc13c15"
version = "0.1.1"

[[Static]]
deps = ["IfElse"]
git-tree-sha1 = "a8f30abc7c64a39d389680b74e749cf33f872a70"
uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
version = "0.3.3"

[[StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"]
git-tree-sha1 = "3c76dde64d03699e074ac02eb2e8ba8254d428da"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
version = "1.2.13"

[[Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[[StatsAPI]]
git-tree-sha1 = "1958272568dc176a1d881acb797beb909c785510"
uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
version = "1.0.0"

[[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
git-tree-sha1 = "65fb73045d0e9aaa39ea9a29a5e7506d9ef6511f"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.33.11"

[[TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"

[[Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"

[[TensorCore]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6"
uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50"
version = "0.1.1"

[[Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[TiffImages]]
deps = ["ColorTypes", "DocStringExtensions", "FileIO", "FixedPointNumbers", "IndirectArrays", "Inflate", "OffsetArrays", "OrderedCollections", "PkgVersion", "ProgressMeter"]
git-tree-sha1 = "945b8d87c5e8d5e34e6207ee15edb9d11ae44716"
uuid = "731e570b-9d59-4bfa-96dc-6df516fadf69"
version = "0.4.3"

[[TiledIteration]]
deps = ["OffsetArrays"]
git-tree-sha1 = "5683455224ba92ef59db72d10690690f4a8dc297"
uuid = "06e1c1a7-607b-532d-9fad-de7d9aa2abac"
version = "0.3.1"

[[TimerOutputs]]
deps = ["ExprTools", "Printf"]
git-tree-sha1 = "7cb456f358e8f9d102a8b25e8dfedf58fa5689bc"
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
version = "0.5.13"

[[TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "216b95ea110b5972db65aa90f88d8d89dcb8851c"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.6"

[[URIParser]]
deps = ["Unicode"]
git-tree-sha1 = "53a9f49546b8d2dd2e688d216421d050c9a31d0d"
uuid = "30578b45-9adc-5946-b283-645ec420af67"
version = "0.4.1"

[[URIs]]
git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.3.0"

[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[VersionParsing]]
git-tree-sha1 = "80229be1f670524750d905f8fc8148e5a8c4537f"
uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
version = "1.2.0"

[[WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "9398e8fefd83bde121d5127114bd3b6762c764a6"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.4"

[[ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "3593e69e469d2111389a9bd06bac1f3d730ac6de"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.9.4"

[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"

[[Zygote]]
deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "78bdfa26eb61600038461229bcd7a5b6f6bb32e4"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.26"

[[ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.2"

[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
"""

# ╔═╡ Cell order:
# ╟─cc864502-6eec-4c92-ace8-f7d72fd66e8f
# ╠═b02f39a2-2b91-11ec-0131-3d5c8629c0a1
# ╟─c74eb080-1d62-4d10-b596-d593dc580145
# ╠═b9f67b96-6896-45db-9d1b-79ca6112612a
# ╟─9cf6e113-e799-4a4e-92e3-84e51eb8e74f
# ╠═8fee236e-c453-489e-b792-9e684b1d1141
# ╟─6870a7f2-2bef-405d-a31d-89bb05ed18cf
# ╠═824347c4-1cf8-4794-8821-c07ebb5a2892
# ╟─f0720c68-3582-4c23-885b-80937693b5a3
# ╠═ed63f032-6d9d-48e3-a2cf-b22f2c77f6b2
# ╠═9a5f495f-ef25-44ed-8364-ef06ad49e461
# ╟─bfed0f44-0f53-4344-8280-cebeebfd59b2
# ╠═fcb5b8e2-e713-4f39-ae02-5db8e8f70454
# ╟─8a8c6d53-8542-4755-9074-04c22f2171d2
# ╠═8143d560-60d2-41e4-a6da-f4480fc3d879
# ╟─251678dc-5e44-444f-946e-7e16b2f30ed0
# ╟─ac490092-cd19-417e-9e77-6935b8feb6eb
# ╠═bc7caa57-2adb-4270-9814-bdefadc082bf
# ╟─51dfb587-d083-4986-bcde-74bbf32ad3ae
# ╠═e7934634-7d6e-4e20-9575-e46fb7749786
# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002
