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‎.gitignore

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docs/site/
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docs/flux.css
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deps
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Manifest.toml

‎.travis.yml

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- linux
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# - osx
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julia:
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- 0.7
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- 1.0
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- nightly
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# uncomment the following lines to override the default test script
@@ -15,5 +14,5 @@ matrix:
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allow_failures:
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- julia: nightly
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after_success:
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- julia -e 'using Pkg; Pkg.add("Documenter"); Pkg.add("NNlib")'
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- julia -e 'using Pkg; ps=Pkg.PackageSpec(name="Documenter", version="0.19"); Pkg.add(ps); Pkg.pin(ps); Pkg.add("NNlib")'
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- julia -e 'using Pkg; cd(Pkg.dir("Flux")); include(joinpath("docs", "make.jl"))'

‎Manifest.toml

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‎Project.toml

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name = "Flux"
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uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
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[deps]
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DiffRules = "b552c78f-8df3-52c6-915a-8e097449b14b"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
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NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
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SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
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ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"

‎REQUIRE

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julia 0.7
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julia 1.0
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Juno
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MacroTools 0.3.3
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NNlib

‎docs/src/internals/tracker.md

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Firstly, we must tell the tracker system to stop when it sees a call to `minus`, and record it. We can do this using dispatch:
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```julia
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using Flux.Tracker: TrackedReal, track, @grad
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using Flux.Tracker: TrackedArray, track, @grad
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minus(a::TrackedArray, b::TrackedArray) = Tracker.track(minus, a, b)
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minus(a::TrackedArray, b::TrackedArray) = track(minus, a, b)
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```
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`track` takes care of building a new `Tracked` object and recording the operation on the tape. We just need to provide a gradient definition.
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```julia
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@grad function minus(a, b)
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return minus(data(a),data(b)), Δ -> (Δ, -Δ)
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return minus(data(a), data(b)), Δ -> (Δ, -Δ)
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end
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```
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@grad a * b = data(a)*data(b), Δ -> (Δ*b, a*Δ)
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```
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We can then calculate the first derivative of `minus` as follows:
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```julia
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a = param([1,2,3])
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b = param([3,2,1])
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c = minus(a, b) # [-2.0 (tracked), 0.0 (tracked), 2.0 (tracked)]
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Tracker.back!(c, 1)
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Tracker.grad(a) # [1.00, 1.00, 1.00]
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Tracker.grad(b) # [-1.00, -1.00, -1.00]
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```
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For multi-argument functions with custom gradients, you likely want to catch not just `minus(::TrackedArray, ::TrackedArray)` but also `minus(::Array, TrackedArray)` and so on. To do so, just define those extra signatures as needed:
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```julia

‎docs/src/models/layers.md

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MeanPool
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```
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## Additional Convolution Layers
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```@docs
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DepthwiseConv
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```
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## Recurrent Layers
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Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).

‎src/Flux.jl

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using MacroTools, Juno, Requires, Reexport, Statistics, Random
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using MacroTools: @forward
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export Chain, Dense, RNN, LSTM, GRU, Conv, CrossCor, MaxPool, MeanPool,
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Dropout, LayerNorm, BatchNorm,
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params, mapleaves, cpu, gpu
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export Chain, Dense, RNN, LSTM, GRU, Conv, MaxPool, MeanPool,
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DepthwiseConv, Dropout, LayerNorm, BatchNorm,
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params, mapleaves, cpu, gpu, CrossCor
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@reexport using NNlib
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include("optimise/Optimise.jl")
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export SGD, ADAM, ADAMW, AdaMax, Momentum, Nesterov,
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RMSProp, ADAGrad, ADADelta, AMSGrad, NADAM
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export SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
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ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM,
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ADAMW, InvDecay, ExpDecay, WeightDecay
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include("utils.jl")
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include("onehot.jl")

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