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This commit is contained in:
Dhairya Gandhi 2020-04-29 16:11:39 +05:30
parent d8e44fcc1c 9237cdaf5b
commit 5086c0f4f0
47 changed files with 1904 additions and 769 deletions
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11
.travis.yml
View File

@ -7,16 +7,16 @@ os:
julia:
- 1.3
- 1
- nightly
matrix:
allow_failures:
- julia: nightly
notifications:
email: false
jobs:
include:
- stage: "Documentation"
julia: 1.3
julia: 1
os: linux
script:
- julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));
@ -24,6 +24,9 @@ jobs:
- julia --project=docs/ docs/make.jl
after_success: skip
allow_failures:
- julia: nightly
## uncomment the following lines to override the default test script
script:
- julia --color=yes -e 'using Pkg; Pkg.activate(); Pkg.instantiate(); Pkg.test()'

173
Manifest.toml
View File

@ -8,15 +8,21 @@ version = "0.5.0"
[[AbstractTrees]]
deps = ["Markdown"]
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git-tree-sha1 = "86d092c2599f1f7bb01668bf8eb3412f98d61e47"
uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
version = "0.3.0"
version = "0.3.2"
[[Adapt]]
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git-tree-sha1 = "c88cfc7f9c1f9f8633cddf0b56e86302b70f64c5"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
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version = "1.0.1"
[[ArrayLayouts]]
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version = "0.2.3"
[[Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
@ -34,39 +40,45 @@ version = "0.2.0"
[[CUDAapi]]
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version = "4.0.0"
[[CUDAdrv]]
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version = "5.0.1"
version = "6.2.2"
[[CUDAnative]]
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deps = ["Adapt", "BinaryProvider", "CEnum", "CUDAapi", "CUDAdrv", "Cthulhu", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "MacroTools", "Pkg", "Printf", "TimerOutputs"]
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[[ColorTypes]]
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version = "0.10.0"
[[Colors]]
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deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
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version = "0.12.0"
[[CommonSubexpressions]]
deps = ["Test"]
@ -74,11 +86,23 @@ git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0"
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version = "0.2.0"
[[CompilerSupportLibraries_jll]]
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[[Cthulhu]]
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version = "1.0.1"
[[CuArrays]]
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git-tree-sha1 = "51fbe053dea29ed2513e02d38380007310cf4c4b"
deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "Libdl", "LinearAlgebra", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SparseArrays", "Statistics", "TimerOutputs"]
git-tree-sha1 = "e8c55b38dcca955f5aed8ec4479cdc95810db1e1"
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version = "1.6.0"
version = "2.0.1"
[[DataAPI]]
git-tree-sha1 = "674b67f344687a88310213ddfa8a2b3c76cc4252"
@ -87,9 +111,9 @@ version = "1.1.0"
[[DataStructures]]
deps = ["InteractiveUtils", "OrderedCollections"]
git-tree-sha1 = "f784254f428fb8fd7ac15982e5862a38a44523d3"
git-tree-sha1 = "73eb18320fe3ba58790c8b8f6f89420f0a622773"
uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
version = "0.17.7"
version = "0.17.11"
[[Dates]]
deps = ["Printf"]
@ -107,78 +131,61 @@ version = "1.0.2"
[[DiffRules]]
deps = ["NaNMath", "Random", "SpecialFunctions"]
git-tree-sha1 = "10dca52cf6d4a62d82528262921daf63b99704a2"
git-tree-sha1 = "eb0c34204c8410888844ada5359ac8b96292cfd1"
uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
version = "1.0.0"
version = "1.0.1"
[[Distributed]]
deps = ["Random", "Serialization", "Sockets"]
uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
[[FFTW]]
deps = ["AbstractFFTs", "FFTW_jll", "IntelOpenMP_jll", "Libdl", "LinearAlgebra", "MKL_jll", "Reexport"]
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version = "1.2.0"
[[FFTW_jll]]
deps = ["Libdl", "Pkg"]
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uuid = "f5851436-0d7a-5f13-b9de-f02708fd171a"
version = "3.3.9+3"
[[FillArrays]]
deps = ["LinearAlgebra", "Random", "SparseArrays"]
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version = "0.8.7"
[[FixedPointNumbers]]
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version = "0.8.0"
[[ForwardDiff]]
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version = "0.10.10"
[[GPUArrays]]
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version = "3.1.0"
[[IRTools]]
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version = "0.3.0"
[[IntelOpenMP_jll]]
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uuid = "1d5cc7b8-4909-519e-a0f8-d0f5ad9712d0"
version = "2018.0.3+0"
version = "0.3.1"
[[InteractiveUtils]]
deps = ["Markdown"]
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
[[Juno]]
deps = ["Base64", "Logging", "Media", "Profile", "Test"]
git-tree-sha1 = "30d94657a422d09cb97b6f86f04f750fa9c50df8"
deps = ["Base64", "Logging", "Media", "Profile"]
git-tree-sha1 = "e1ba2a612645b3e07c773c3a208f215745081fe6"
uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
version = "0.7.2"
version = "0.8.1"
[[LLVM]]
deps = ["CEnum", "Libdl", "Printf", "Unicode"]
git-tree-sha1 = "1d08d7e4250f452f6cb20e4574daaebfdbee0ff7"
git-tree-sha1 = "b6b86801ae2f2682e0a4889315dc76b68db2de71"
uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
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version = "1.3.4"
[[LibGit2]]
deps = ["Printf"]
uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
[[Libdl]]
@ -191,17 +198,11 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
[[Logging]]
uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
[[MKL_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "61069ae718b8ab1e325bbfb4e5268902e7ea08e3"
uuid = "856f044c-d86e-5d09-b602-aeab76dc8ba7"
version = "2019.0.117+0"
[[MacroTools]]
deps = ["DataStructures", "Markdown", "Random"]
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deps = ["Markdown", "Random"]
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uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
version = "0.5.3"
version = "0.5.5"
[[Markdown]]
deps = ["Base64"]
@ -224,9 +225,9 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
[[NNlib]]
deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics"]
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git-tree-sha1 = "d9f196d911f55aeaff11b11f681b135980783824"
uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
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version = "0.6.6"
[[NaNMath]]
git-tree-sha1 = "928b8ca9b2791081dc71a51c55347c27c618760f"
@ -234,10 +235,10 @@ uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
version = "0.3.3"
[[OpenSpecFun_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "65f672edebf3f4e613ddf37db9dcbd7a407e5e90"
deps = ["CompilerSupportLibraries_jll", "Libdl", "Pkg"]
git-tree-sha1 = "d51c416559217d974a1113522d5919235ae67a87"
uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
version = "0.5.3+1"
version = "0.5.3+3"
[[OrderedCollections]]
deps = ["Random", "Serialization", "Test"]
@ -273,9 +274,9 @@ version = "0.2.0"
[[Requires]]
deps = ["UUIDs"]
git-tree-sha1 = "999513b7dea8ac17359ed50ae8ea089e4464e35e"
git-tree-sha1 = "d37400976e98018ee840e0ca4f9d20baa231dc6b"
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
version = "1.0.0"
version = "1.0.1"
[[SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
@ -298,9 +299,9 @@ uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[[SpecialFunctions]]
deps = ["OpenSpecFun_jll"]
git-tree-sha1 = "268052ee908b2c086cc0011f528694f02f3e2408"
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version = "0.9.0"
version = "0.10.0"
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deps = ["LinearAlgebra", "Random", "Statistics"]
@ -314,9 +315,9 @@ uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
[[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
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git-tree-sha1 = "a6102b1f364befdb05746f386b67c6b7e3262c45"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.32.0"
version = "0.33.0"
[[Test]]
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
@ -343,21 +344,21 @@ uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
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git-tree-sha1 = "8748302cfdec02c4ae9c97b112cf10003f7f767f"
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version = "0.9.1"
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version = "1.2.11+9"
[[Zygote]]
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[[ZygoteRules]]
deps = ["MacroTools"]

3
NEWS.md
View File

@ -1,3 +1,6 @@
# v0.10.5
* Add option for [same padding](https://github.com/FluxML/Flux.jl/pull/901) to conv and pooling layers by setting `pad=SamePad()`.
# v0.10.0
* The default AD engine has switched from [Tracker to Zygote.jl](https://github.com/FluxML/Flux.jl/pull/669)
- The dependency on Tracker.jl has been removed.

16
Project.toml
View File

@ -1,6 +1,6 @@
name = "Flux"
uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
version = "0.10.2"
version = "0.10.4"
[deps]
AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@ -26,21 +26,23 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
[compat]
AbstractTrees = "0.2, 0.3"
Adapt = "1"
CodecZlib = "0.5, 0.6"
Colors = "0.8, 0.9, 0.10, 0.11"
CuArrays = "1.6"
CodecZlib = "0.5, 0.6, 0.7"
Colors = "0.8, 0.9, 0.10, 0.11, 0.12"
CuArrays = "2"
Juno = "0.5, 0.6, 0.7, 0.8"
MacroTools = "0.3, 0.4, 0.5"
NNlib = "0.6"
Reexport = "0.2"
StatsBase = "0"
ZipFile = "0.7, 0.8, 0.9"
Zygote = "0.4"
julia = "1"
Zygote = "0.4.13"
julia = "1.3"
[extras]
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[targets]
test = ["Test", "Documenter"]
test = ["Test", "Documenter", "IterTools", "LinearAlgebra"]

89
docs/Manifest.toml
View File

@ -1,89 +0,0 @@
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version = "0.3.6"
[[Pkg]]
deps = ["Dates", "LibGit2", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
[[Printf]]
deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
[[REPL]]
deps = ["InteractiveUtils", "Markdown", "Sockets"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
[[Random]]
deps = ["Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[[SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
[[Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
[[Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
[[Test]]
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

4
docs/Project.toml
View File

@ -1,2 +1,6 @@
[deps]
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
[compat]
Documenter = "0.24"

31
docs/make.jl
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@ -1,29 +1,36 @@
using Pkg;
Pkg.activate(joinpath(@__DIR__, "..")); Pkg.instantiate()
Pkg.activate(); Pkg.instantiate()
pushfirst!(LOAD_PATH, joinpath(@__DIR__, ".."))
using Documenter, Flux, NNlib
DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive=true)
makedocs(modules=[Flux, NNlib],
doctest = VERSION >= v"1.4",
sitename = "Flux",
pages = ["Home" => "index.md",
"Building Models" =>
["Basics" => "models/basics.md",
"Recurrence" => "models/recurrence.md",
"Regularisation" => "models/regularisation.md",
"Model Reference" => "models/layers.md"],
"Model Reference" => "models/layers.md",
"Advanced Model Building" => "models/advanced.md",
"NNlib" => "models/nnlib.md"],
"Handling Data" =>
["One-Hot Encoding" => "data/onehot.md",
"DataLoader" => "data/dataloader.md"],
"Training Models" =>
["Optimisers" => "training/optimisers.md",
"Training" => "training/training.md"],
"One-Hot Encoding" => "data/onehot.md",
"GPU Support" => "gpu.md",
"Saving & Loading" => "saving.md",
"The Julia Ecosystem" => "ecosystem.md",
"Utility Functions" => "utilities.md",
"Performance Tips" => "performance.md",
"Datasets" => "datasets.md",
"Community" => "community.md"],
format = Documenter.HTML(assets = ["assets/flux.css"],
analytics = "UA-36890222-9",
prettyurls = haskey(ENV, "CI")))
format = Documenter.HTML(
analytics = "UA-36890222-9",
assets = ["assets/flux.css"],
prettyurls = get(ENV, "CI", nothing) == "true"),
)
deploydocs(repo = "github.com/FluxML/Flux.jl.git")
deploydocs(repo = "github.com/FluxML/Flux.jl.git",
target = "build",
push_preview = true)

6
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@ -0,0 +1,6 @@
# DataLoader
Flux provides the `DataLoader` type in the `Flux.Data` module to handle iteration over mini-batches of data.
```@docs
Flux.Data.DataLoader
```

9
docs/src/data/onehot.md
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@ -31,6 +31,11 @@ julia> onecold([0.3, 0.2, 0.5], [:a, :b, :c])
:c
```
```@docs
Flux.onehot
Flux.onecold
```
## Batches
`onehotbatch` creates a batch (matrix) of one-hot vectors, and `onecold` treats matrices as batches.
@ -52,3 +57,7 @@ julia> onecold(ans, [:a, :b, :c])
```
Note that these operations returned `OneHotVector` and `OneHotMatrix` rather than `Array`s. `OneHotVector`s behave like normal vectors but avoid any unnecessary cost compared to using an integer index directly. For example, multiplying a matrix with a one-hot vector simply slices out the relevant row of the matrix under the hood.
```@docs
Flux.onehotbatch
```

20
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@ -0,0 +1,20 @@
# Datasets
Flux includes several standard machine learning datasets.
```@docs
Flux.Data.Iris.features()
Flux.Data.Iris.labels()
Flux.Data.MNIST.images()
Flux.Data.MNIST.labels()
Flux.Data.FashionMNIST.images()
Flux.Data.FashionMNIST.labels()
Flux.Data.CMUDict.phones()
Flux.Data.CMUDict.symbols()
Flux.Data.CMUDict.rawdict()
Flux.Data.CMUDict.cmudict()
Flux.Data.Sentiment.train()
Flux.Data.Sentiment.test()
Flux.Data.Sentiment.dev()
```

21
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@ -0,0 +1,21 @@
# The Julia Ecosystem
One of the main strengths of Julia lies in an ecosystem of packages
globally providing a rich and consistent user experience.
This is a non-exhaustive list of Julia packages, nicely complementing `Flux` in typical
machine learning and deep learning workflows:
- [ArgParse.jl](https://github.com/carlobaldassi/ArgParse.jl): package for parsing command-line arguments to Julia programs.
- [Augmentor.jl](https://github.com/Evizero/Augmentor.jl): a fast image augmentation library in Julia for machine learning.
- [BSON.jl](https://github.com/JuliaIO/BSON.jl): package for working with the Binary JSON serialisation format
- [DataFrames.jl](https://github.com/joshday/OnlineStats.jl): in-memory tabular data in Julia
- [DrWatson.jl](https://github.com/JuliaDynamics/DrWatson.jl): a scientific project assistant software
- [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl): utility package for accessing common machine learning datasets
- [OnlineStats.jl](https://github.com/joshday/OnlineStats.jl): single-pass algorithms for statistics
- [Parameters.jl](https://github.com/mauro3/Parameters.jl): types with default field values, keyword constructors and (un-)pack macros
- [ProgressMeters.jl](https://github.com/timholy/ProgressMeter.jl): progress meters for long-running computations
- [TensorBoardLogger.jl](https://github.com/PhilipVinc/TensorBoardLogger.jl): easy peasy logging to [tensorboard](https://www.tensorflow.org/tensorboard) in Julia
This tight integration among Julia pakages is shown in some of the examples in the [model-zoo](https://github.com/FluxML/model-zoo) repository.

4
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@ -30,7 +30,7 @@ If you define a structured model, like a `Dense` layer or `Chain`, you just need
```julia
d = Dense(10, 5, σ)
d = fmap(cu, d)
d.W # Tracked CuArray
d.W # CuArray
d(cu(rand(10))) # CuArray output
m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
@ -53,7 +53,7 @@ julia> x = rand(10) |> gpu
0.511655
julia> m(x)
Tracked 5-element CuArray{Float32,1}:
5-element CuArray{Float32,1}:
-0.30535
⋮
-0.618002

73
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@ -0,0 +1,73 @@
# Advanced Model Building and Customisation
Here we will try and describe usage of some more advanced features that Flux provides to give more control over model building.
## Customising Parameter Collection for a Model
Taking reference from our example `Affine` layer from the [basics](basics.md#Building-Layers-1).
By default all the fields in the `Affine` type are collected as its parameters, however, in some cases it may be desired to hold other metadata in our "layers" that may not be needed for training, and are hence supposed to be ignored while the parameters are collected. With Flux, it is possible to mark the fields of our layers that are trainable in two ways.
The first way of achieving this is through overloading the `trainable` function.
```julia-repl
julia> @functor Affine
julia> a = Affine(rand(3,3), rand(3))
Affine{Array{Float64,2},Array{Float64,1}}([0.66722 0.774872 0.249809; 0.843321 0.403843 0.429232; 0.683525 0.662455 0.065297], [0.42394, 0.0170927, 0.544955])
julia> Flux.params(a) # default behavior
Params([[0.66722 0.774872 0.249809; 0.843321 0.403843 0.429232; 0.683525 0.662455 0.065297], [0.42394, 0.0170927, 0.544955]])
julia> Flux.trainable(a::Affine) = (a.W, a.b,)
julia> Flux.params(a)
Params([[0.66722 0.774872 0.249809; 0.843321 0.403843 0.429232; 0.683525 0.662455 0.065297]])
```
Only the fields returned by `trainable` will be collected as trainable parameters of the layer when calling `Flux.params`.
Another way of achieving this is through the `@functor` macro directly. Here, we can mark the fields we are interested in by grouping them in the second argument:
```julia
Flux.@functor Affine (W,)
```
However, doing this requires the `struct` to have a corresponding constructor that accepts those parameters.
## Freezing Layer Parameters
When it is desired to not include all the model parameters (for e.g. transfer learning), we can simply not pass in those layers into our call to `params`.
Consider a simple multi-layer perceptron model where we want to avoid optimising the first two `Dense` layers. We can obtain
this using the slicing features `Chain` provides:
```julia
m = Chain(
Dense(784, 64, relu),
Dense(64, 64, relu),
Dense(32, 10)
)
ps = Flux.params(m[3:end])
```
The `Zygote.Params` object `ps` now holds a reference to only the parameters of the layers passed to it.
During training, the gradients will only be computed for (and applied to) the last `Dense` layer, therefore only that would have its parameters changed.
`Flux.params` also takes multiple inputs to make it easy to collect parameters from heterogenous models with a single call. A simple demonstration would be if we wanted to omit optimising the second `Dense` layer in the previous example. It would look something like this:
```julia
Flux.params(m[1], m[3:end])
```
Sometimes, a more fine-tuned control is needed.
We can freeze a specific parameter of a specific layer which already entered a `Params` object `ps`,
by simply deleting it from `ps`:
```julia
ps = params(m)
delete!(ps, m[2].b)
```

8
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@ -69,8 +69,8 @@ b = rand(2)
predict(x) = W*x .+ b
function loss(x, y)
ŷ = predict(x)
sum((y .- ŷ).^2)
ŷ = predict(x)
sum((y .- ŷ).^2)
end
x, y = rand(5), rand(2) # Dummy data
@ -220,6 +220,8 @@ Flux.@functor Affine
This enables a useful extra set of functionality for our `Affine` layer, such as [collecting its parameters](../training/optimisers.md) or [moving it to the GPU](../gpu.md).
For some more helpful tricks, including parameter freezing, please checkout the [advanced usage guide](advanced.md).
## Utility functions
Flux provides some utility functions to help you generate models in an automated fashion.
@ -238,5 +240,5 @@ Currently limited to the following layers:
- `MeanPool`
```@docs
outdims
Flux.outdims
```

51
docs/src/models/layers.md
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@ -14,10 +14,13 @@ These layers are used to build convolutional neural networks (CNNs).
```@docs
Conv
MaxPool
GlobalMaxPool
MeanPool
GlobalMeanPool
DepthwiseConv
ConvTranspose
CrossCor
flatten
```
## Recurrent Layers
@ -29,6 +32,7 @@ RNN
LSTM
GRU
Flux.Recur
Flux.reset!
```
## Other General Purpose Layers
@ -40,40 +44,45 @@ Maxout
SkipConnection
```
## Activation Functions
Non-linearities that go between layers of your model. Most of these functions are defined in [NNlib](https://github.com/FluxML/NNlib.jl) but are available by default in Flux.
Note that, unless otherwise stated, activation functions operate on scalars. To apply them to an array you can call `σ.(xs)`, `relu.(xs)` and so on.
```@docs
σ
relu
leakyrelu
elu
swish
```
## Normalisation & Regularisation
These layers don't affect the structure of the network but may improve training times or reduce overfitting.
```@docs
Flux.normalise
BatchNorm
Flux.dropout
Dropout
AlphaDropout
LayerNorm
InstanceNorm
GroupNorm
```
### Testmode
Many normalisation layers behave differently under training and inference (testing). By default, Flux will automatically determine when a layer evaluation is part of training or inference. Still, depending on your use case, it may be helpful to manually specify when these layers should be treated as being trained or not. For this, Flux provides `Flux.testmode!`. When called on a model (e.g. a layer or chain of layers), this function will place the model into the mode specified.
```@docs
Flux.testmode!
trainmode!
```
## Cost Functions
```@docs
mse
crossentropy
logitcrossentropy
binarycrossentropy
logitbinarycrossentropy
kldivergence
poisson
hinge
Flux.mae
Flux.mse
Flux.msle
Flux.huber_loss
Flux.crossentropy
Flux.logitcrossentropy
Flux.binarycrossentropy
Flux.logitbinarycrossentropy
Flux.kldivergence
Flux.poisson
Flux.hinge
Flux.squared_hinge
Flux.dice_coeff_loss
Flux.tversky_loss
```

61
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@ -0,0 +1,61 @@
# NNlib
Flux re-exports all of the functions exported by the [NNlib](https://github.com/FluxML/NNlib.jl) package.
## Activation Functions
Non-linearities that go between layers of your model. Note that, unless otherwise stated, activation functions operate on scalars. To apply them to an array you can call `σ.(xs)`, `relu.(xs)` and so on.
```@docs
NNlib.celu
NNlib.elu
NNlib.gelu
NNlib.hardsigmoid
NNlib.hardtanh
NNlib.leakyrelu
NNlib.lisht
NNlib.logcosh
NNlib.logsigmoid
NNlib.mish
NNlib.relu
NNlib.relu6
NNlib.rrelu
NNlib.selu
NNlib.sigmoid
NNlib.softplus
NNlib.softshrink
NNlib.softsign
NNlib.swish
NNlib.tanhshrink
NNlib.trelu
```
## Softmax
```@docs
NNlib.softmax
NNlib.logsoftmax
```
## Pooling
```@docs
NNlib.maxpool
NNlib.meanpool
```
## Convolution
```@docs
NNlib.conv
NNlib.depthwiseconv
```
## Batched Operations
```@docs
NNlib.batched_mul
NNlib.batched_mul!
NNlib.batched_adjoint
NNlib.batched_transpose
```

8
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@ -31,7 +31,7 @@ julia> params(m)
param([0.0, 0.0, 0.0, 0.0, 0.0])
julia> sum(norm, params(m))
26.01749952921026 (tracked)
26.01749952921026
```
Here's a larger example with a multi-layer perceptron.
@ -52,7 +52,7 @@ One can also easily add per-layer regularisation via the `activations` function:
```julia
julia> using Flux: activations
julia> c = Chain(Dense(10,5,σ),Dense(5,2),softmax)
julia> c = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
julia> activations(c, rand(10))
@ -64,3 +64,7 @@ julia> activations(c, rand(10))
julia> sum(norm, ans)
2.1166067f0
```
```@docs
Flux.activations
```

19
docs/src/performance.md
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@ -4,7 +4,7 @@ All the usual [Julia performance tips apply](https://docs.julialang.org/en/v1/ma
As always [profiling your code](https://docs.julialang.org/en/v1/manual/profile/#Profiling-1) is generally a useful way of finding bottlenecks.
Below follow some Flux specific tips/reminders.
## Don't use more precision than you need.
## Don't use more precision than you need
Flux works great with all kinds of number types.
But often you do not need to be working with say `Float64` (let alone `BigFloat`).
@ -14,7 +14,8 @@ Which means allocations occur much faster.
And you use less memory.
## Make sure your activation and loss functions preserve the type of their inputs
## Preserve inputs' types
Not only should your activation and loss functions be [type-stable](https://docs.julialang.org/en/v1/manual/performance-tips/#Write-%22type-stable%22-functions-1),
they should also preserve the type of their inputs.
@ -29,31 +30,29 @@ because it results in having to use slow mixed type multiplication in the dense
Similar situations can occur in the loss function during backpropagation.
Which means if you change your data say from `Float64` to `Float32` (which should give a speedup: see above),
you will see a large slow-down
you will see a large slow-down.
This can occur sneakily, because you can cause type-promotion by interacting with a numeric literals.
E.g. the following will have run into the same problem as above:
```
leaky_tanh(x) = 0.01x + tanh(x)
leaky_tanh(x) = 0.01*x + tanh(x)
```
While one could change your activation function (e.g. to use `0.01f0x`) to avoid this when ever your inputs change,
the idiomatic (and safe way) is to use `oftype`.
While one could change the activation function (e.g. to use `0.01f0x`), the idiomatic (and safe way) to avoid type casts whenever inputs changes is to use `oftype`:
```
leaky_tanh(x) = oftype(x/1, 0.01)x + tanh(x)
leaky_tanh(x) = oftype(x/1, 0.01)*x + tanh(x)
```
## Evaluate batches as Matrices of features, rather than sequences of Vector features
## Evaluate batches as Matrices of features
While it can sometimes be tempting to process your observations (feature vectors) one at a time
e.g.
```julia
function loss_total(xs::AbstractVector{<:Vector}, ys::AbstractVector{<:Vector})
sum(zip(xs, ys)) do (x, y_target)
y_pred = model(x) # evaluate the model
y_pred = model(x) # evaluate the model
return loss(y_pred, y_target)
end
end

12
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@ -21,7 +21,7 @@ grads = gradient(() -> loss(x, y), θ)
We want to update each parameter, using the gradient, in order to improve (reduce) the loss. Here's one way to do that:
```julia
using Flux: update!
using Flux.Optimise: update!
η = 0.1 # Learning Rate
for p in (W, b)
@ -46,11 +46,13 @@ An optimiser `update!` accepts a parameter and a gradient, and updates the param
All optimisers return an object that, when passed to `train!`, will update the parameters passed to it.
```@docs
Flux.Optimise.update!
Descent
Momentum
Nesterov
RMSProp
ADAM
RADAM
AdaMax
ADAGrad
ADADelta
@ -61,7 +63,7 @@ ADAMW
## Optimiser Interface
Flux's optimsers are built around a `struct` that holds all the optimiser parameters along with a definition of how to apply the update rule associated with it. We do this via the `apply!` function which takes the optimiser as the first argument followed by the parameter and its corresponding gradient.
Flux's optimisers are built around a `struct` that holds all the optimiser parameters along with a definition of how to apply the update rule associated with it. We do this via the `apply!` function which takes the optimiser as the first argument followed by the parameter and its corresponding gradient.
In this manner Flux also allows one to create custom optimisers to be used seamlessly. Let's work this with a simple example.
@ -99,15 +101,15 @@ Flux internally calls on this function via the `update!` function. It shares the
## Composing Optimisers
Flux defines a special kind of optimiser called simply as `Optimiser` which takes in a arbitrary optimisers as input. Its behaviour is similar to the usual optimisers, but differs in that it acts by calling the optimisers listed in it sequentially. Each optimiser produces a modified gradient
Flux defines a special kind of optimiser simply called `Optimiser` which takes in arbitrary optimisers as input. Its behaviour is similar to the usual optimisers, but differs in that it acts by calling the optimisers listed in it sequentially. Each optimiser produces a modified gradient
that will be fed into the next, and the resultant update will be applied to the parameter as usual. A classic use case is where adding decays is desirable. Flux defines some basic decays including `ExpDecay`, `InvDecay` etc.
```julia
opt = Optimiser(ExpDecay(0.001, 0.1, 1000, 1e-4), Descent())
```
Here we apply exponential decay to the `Descent` optimser. The defaults of `ExpDecay` say that its learning rate will be decayed every 1000 steps.
It is then applied like any optimser.
Here we apply exponential decay to the `Descent` optimiser. The defaults of `ExpDecay` say that its learning rate will be decayed every 1000 steps.
It is then applied like any optimiser.
```julia
w = randn(10, 10)

26
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@ -7,10 +7,10 @@ To actually train a model we need four things:
* A collection of data points that will be provided to the objective function.
* An [optimiser](optimisers.md) that will update the model parameters appropriately.
With these we can call `Flux.train!`:
With these we can call `train!`:
```julia
Flux.train!(objective, params, data, opt)
```@docs
Flux.Optimise.train!
```
There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo).
@ -32,6 +32,7 @@ Flux.train!(loss, ps, data, opt)
```
The objective will almost always be defined in terms of some *cost function* that measures the distance of the prediction `m(x)` from the target `y`. Flux has several of these built in, like `mse` for mean squared error or `crossentropy` for cross entropy loss, but you can calculate it however you want.
For a list of all built-in loss functions, check out the [layer reference](../models/layers.md).
At first glance it may seem strange that the model that we want to train is not part of the input arguments of `Flux.train!` too. However the target of the optimizer is not the model itself, but the objective function that represents the departure between modelled and observed data. In other words, the model is implicitly defined in the objective function, and there is no need to give it explicitly. Passing the objective function instead of the model and a cost function separately provides more flexibility, and the possibility of optimizing the calculations.
@ -41,6 +42,8 @@ The model to be trained must have a set of tracked parameters that are used to c
Such an object contains a reference to the model's parameters, not a copy, such that after their training, the model behaves according to their updated values.
Handling all the parameters on a layer by layer basis is explained in the [Layer Helpers](../models/basics.md) section. Also, for freezing model parameters, see the [Advanced Usage Guide](../models/advanced.md).
## Datasets
The `data` argument provides a collection of data to train with (usually a set of inputs `x` and target outputs `y`). For example, here's a dummy data set with only one data point:
@ -56,7 +59,8 @@ data = [(x, y)]
```julia
data = [(x, y), (x, y), (x, y)]
# Or equivalently
data = Iterators.repeated((x, y), 3)
using IterTools: ncycle
data = ncycle([(x, y)], 3)
```
It's common to load the `x`s and `y`s separately. In this case you can use `zip`:
@ -67,6 +71,14 @@ ys = [rand( 10), rand( 10), rand( 10)]
data = zip(xs, ys)
```
Training data can be conveniently partitioned for mini-batch training using the [`Flux.Data.DataLoader`](@ref) type:
```julia
X = rand(28, 28, 60000)
Y = rand(0:9, 60000)
data = DataLoader(X, Y, batchsize=128)
```
Note that, by default, `train!` only loops over the data once (a single "epoch").
A convenient way to run multiple epochs from the REPL is provided by `@epochs`.
@ -83,6 +95,10 @@ julia> @epochs 2 Flux.train!(...)
# Train for two epochs
```
```@docs
Flux.@epochs
```
## Callbacks
`train!` takes an additional argument, `cb`, that's used for callbacks so that you can observe the training process. For example:
@ -120,7 +136,7 @@ An example follows that works similar to the default `Flux.train` but with no ca
You don't need callbacks if you just code the calls to your functions directly into the loop.
E.g. in the places marked with comments.
```
```julia
function my_custom_train!(loss, ps, data, opt)
ps = Params(ps)
for d in data

49
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@ -0,0 +1,49 @@
# Utility Functions
Flux contains some utility functions for working with data; these functions
help create inputs for your models or batch your dataset.
Other functions can be used to initialize your layers or to regularly execute
callback functions.
## Working with Data
```@docs
Flux.unsqueeze
Flux.stack
Flux.unstack
Flux.chunk
Flux.frequencies
Flux.batch
Flux.batchseq
Base.rpad(v::AbstractVector, n::Integer, p)
```
## Layer Initialization
These are primarily useful if you are planning to write your own layers.
Flux initializes convolutional layers and recurrent cells with `glorot_uniform`
by default.
To change the default on an applicable layer, pass the desired function with the
`init` keyword. For example:
```jldoctest; setup = :(using Flux)
julia> conv = Conv((3, 3), 1 => 8, relu; init=Flux.glorot_normal)
Conv((3, 3), 1=>8, relu)
```
```@docs
Flux.glorot_uniform
Flux.glorot_normal
```
## Model Abstraction
```@docs
Flux.destructure
```
## Callback Helpers
```@docs
Flux.throttle
Flux.stop
```

31
src/Flux.jl
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@ -7,16 +7,18 @@ using Zygote, MacroTools, Juno, Reexport, Statistics, Random
using MacroTools: @forward
@reexport using NNlib
using Zygote: Params, @adjoint, gradient, pullback, @nograd
export gradient
export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,
export Chain, Dense, Maxout, RNN, LSTM, GRU, SamePad, Conv, CrossCor, ConvTranspose,
GlobalMaxPool, GlobalMeanPool, MaxPool, MeanPool, flatten,
DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm,
SkipConnection, params, fmap, cpu, gpu, f32, f64
SkipConnection, params, fmap, cpu, gpu, f32, f64, testmode!, trainmode!
include("optimise/Optimise.jl")
using .Optimise
using .Optimise: @epochs
export SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
export Descent, ADAM, Momentum, Nesterov, RMSProp,
ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM,
ADAMW, RADAM, InvDecay, ExpDecay, WeightDecay
@ -38,24 +40,13 @@ include("data/Data.jl")
include("deprecations.jl")
include("cuda/cuda.jl")
function __init__()
precompiling = ccall(:jl_generating_output, Cint, ()) != 0
# we don't want to include the CUDA module when precompiling,
# or we could end up replacing it at run time (triggering a warning)
precompiling && return
if !CuArrays.functional()
# nothing to do here, and either CuArrays or one of its dependencies will have warned
else
use_cuda[] = true
# FIXME: this functionality should be conditional at run time by checking `use_cuda`
# (or even better, get moved to CuArrays.jl as much as possible)
if CuArrays.has_cudnn()
include(joinpath(@__DIR__, "cuda/cuda.jl"))
else
@warn "CuArrays.jl did not find libcudnn. Some functionality will not be available."
use_cuda[] = CuArrays.functional() # Can be overridden after load with `Flux.use_cuda[] = false`
if CuArrays.functional()
if !CuArrays.has_cudnn()
@warn "CuArrays.jl found cuda, but did not find libcudnn. Some functionality will not be available."
end
end
end

9
src/data/Data.jl
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@ -3,6 +3,9 @@ module Data
import ..Flux
import SHA
using Random: shuffle!
using Base: @propagate_inbounds
export CMUDict, cmudict
deps(path...) = joinpath(@__DIR__, "..", "..", "deps", path...)
@ -26,6 +29,9 @@ function __init__()
mkpath(deps())
end
include("dataloader.jl")
export DataLoader
include("mnist.jl")
export MNIST
@ -42,4 +48,7 @@ using .Sentiment
include("iris.jl")
export Iris
include("housing.jl")
export Housing
end

25
src/data/cmudict.jl
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@ -24,18 +24,35 @@ function load()
end
end
"""
phones()
Return a `Vector` containing the phones used in the CMU Pronouncing Dictionary.
"""
function phones()
load()
Symbol.(first.(split.(split(read(deps("cmudict", "cmudict.phones"),String),
"\n", keepempty = false), "\t")))
end
"""
symbols()
Return a `Vector` containing the symbols used in the CMU Pronouncing Dictionary.
A symbol is a phone with optional auxiliary symbols, indicating for example the
amount of stress on the phone.
"""
function symbols()
load()
Symbol.(split(read(deps("cmudict", "cmudict.symbols"),String),
"\n", keepempty = false))
end
"""
rawdict()
Return the unfiltered CMU Pronouncing Dictionary.
"""
function rawdict()
load()
Dict(String(xs[1]) => Symbol.(xs[2:end]) for xs in
@ -44,6 +61,14 @@ end
validword(s) = isascii(s) && occursin(r"^[\w\-\.]+$", s)
"""
cmudict()
Return a filtered CMU Pronouncing Dictionary.
It is filtered so each word contains only ASCII characters and a combination of
word characters (as determined by the regex engine using `\\w`), '-' and '.'.
"""
cmudict() = filter(p -> validword(p.first), rawdict())
alphabet() = ['A':'Z'..., '0':'9'..., '_', '-', '.']

92
src/data/dataloader.jl Normal file
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@ -0,0 +1,92 @@
# Adapted from Knet's src/data.jl (author: Deniz Yuret)
struct DataLoader
data
batchsize::Int
nobs::Int
partial::Bool
imax::Int
indices::Vector{Int}
shuffle::Bool
end
"""
DataLoader(data...; batchsize=1, shuffle=false, partial=true)
An object that iterates over mini-batches of `data`, each mini-batch containing `batchsize` observations
(except possibly the last one).
Takes as input one or more data tensors, e.g. X in unsupervised learning, X and Y in
supervised learning. The last dimension in each tensor is considered to be the observation
dimension.
If `shuffle=true`, shuffles the observations each time iterations are re-started.
If `partial=false`, drops the last mini-batch if it is smaller than the batchsize.
The original data is preserved as a tuple in the `data` field of the DataLoader.
Example usage:
Xtrain = rand(10, 100)
train_loader = DataLoader(Xtrain, batchsize=2)
# iterate over 50 mini-batches of size 2
for x in train_loader
@assert size(x) == (10, 2)
...
end
train_loader.data # original dataset
Xtrain = rand(10, 100)
Ytrain = rand(100)
train_loader = DataLoader(Xtrain, Ytrain, batchsize=2, shuffle=true)
for epoch in 1:100
for (x, y) in train_loader
@assert size(x) == (10, 2)
@assert size(y) == (2,)
...
end
end
# train for 10 epochs
using IterTools: ncycle
Flux.train!(loss, ps, ncycle(train_loader, 10), opt)
"""
function DataLoader(data...; batchsize=1, shuffle=false, partial=true)
length(data) > 0 || throw(ArgumentError("Need at least one data input"))
batchsize > 0 || throw(ArgumentError("Need positive batchsize"))
nx = size(data[1])[end]
for i=2:length(data)
nx != size(data[i])[end] && throw(DimensionMismatch("All data should contain same number of observations"))
end
if nx < batchsize
@warn "Number of data points less than batchsize, decreasing the batchsize to $nx"
batchsize = nx
end
imax = partial ? nx : nx - batchsize + 1
ids = 1:min(nx, batchsize)
DataLoader(data, batchsize, nx, partial, imax, [1:nx;], shuffle)
end
getdata(x::AbstractArray, ids) = x[(Base.Colon() for _=1:ndims(x)-1)..., ids]
@propagate_inbounds function Base.iterate(d::DataLoader, i=0) # returns data in d.indices[i+1:i+batchsize]
i >= d.imax && return nothing
if d.shuffle && i == 0
shuffle!(d.indices)
end
nexti = min(i + d.batchsize, d.nobs)
ids = d.indices[i+1:nexti]
if length(d.data) == 1
batch = getdata(d.data[1], ids)
else
batch = ((getdata(x, ids) for x in d.data)...,)
end
return (batch, nexti)
end
function Base.length(d::DataLoader)
n = d.nobs / d.batchsize
d.partial ? ceil(Int,n) : floor(Int,n)
end

9
src/data/fashion-mnist.jl
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@ -33,9 +33,10 @@ const TESTLABELS = joinpath(dir, "t10k-labels-idx1-ubyte")
Load the Fashion-MNIST images.
Each image is a 28×28 array of `Gray` colour values (see Colors.jl).
Each image is a 28×28 array of `Gray` colour values
(see [Colors.jl](https://github.com/JuliaGraphics/Colors.jl)).
Returns the 60,000 training images by default; pass `:test` to retreive the
Return the 60,000 training images by default; pass `:test` to retrieve the
10,000 test images.
"""
function images(set = :train)
@ -49,10 +50,10 @@ end
labels()
labels(:test)
Load the labels corresponding to each of the images returned from `images()`.
Load the labels corresponding to each of the images returned from [`images()`](@ref).
Each label is a number from 0-9.
Returns the 60,000 training labels by default; pass `:test` to retreive the
Return the 60,000 training labels by default; pass `:test` to retrieve the
10,000 test labels.
"""
function labels(set = :train)

136
src/data/housing.jl Normal file
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@ -0,0 +1,136 @@
"""
1. Title: Boston Housing Data
2. Sources:
(a) Origin: This dataset was taken from the StatLib library which is
maintained at Carnegie Mellon University.
(b) Creator: Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the
demand for clean air', J. Environ. Economics & Management,
vol.5, 81-102, 1978.
(c) Date: July 7, 1993
3. Number of Instances: 506
4. Number of Attributes: 13 continuous attributes (including "class"
attribute "MEDV"), 1 binary-valued attribute.
5. Attribute Information:
1. CRIM per capita crime rate by town
2. ZN proportion of residential land zoned for lots over
25,000 sq.ft.
3. INDUS proportion of non-retail business acres per town
4. CHAS Charles River dummy variable (= 1 if tract bounds
river; 0 otherwise)
5. NOX nitric oxides concentration (parts per 10 million)
6. RM average number of rooms per dwelling
7. AGE proportion of owner-occupied units built prior to 1940
8. DIS weighted distances to five Boston employment centres
9. RAD index of accessibility to radial highways
10. TAX full-value property-tax rate per 10,000 dollars
11. PTRATIO pupil-teacher ratio by town
12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks
by town
13. LSTAT % lower status of the population
14. MEDV Median value of owner-occupied homes in 1000's of dollars
Downloaded From: https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data
"""
module Housing
using DelimitedFiles
using ..Data: deps, download_and_verify
#Uncomment if package exists
#const cache_prefix = "https://cache.julialang.org/"
const cache_prefix = ""
function load()
isfile(deps("housing.data")) && return
@info "Downloading the Boston housing Dataset"
download_and_verify("$(cache_prefix)http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data",
deps("housing.data"),
"baadf72995725d76efe787b664e1f083388c79ba21ef9a7990d87f774184735a")
#@info "Download complete. Working on the files"
path = deps()
isfile(deps("housing.data")) && touch(joinpath(path, "tempfile.data"))
open(joinpath(path, "tempfile.data"), "a") do fout
open(deps("housing.data"), "r") do fin
for line in eachline(fin)
line = replace(lstrip(line), r" +" => s",")
println(fout, line)
end
end
end
mv(joinpath(path, "tempfile.data"), deps("housing.data"), force=true)
end
"""
Gets the targets for the Boston housing dataset, a 506 element array listing the targets for each example
```jldoctest
julia> using Flux
julia> target = Flux.Data.Housing.targets()
julia> summary(target)
506×1 Array{Float64,2}
julia> target[1]
24.0
"""
function targets()
load()
housing = readdlm(deps("housing.data"), ',')
reshape(Vector{Float64}(housing[1:end,end]), (506, 1))
end
"""
Gets the names of the features provided in the dataset
"""
function feature_names()
["crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat"]
end
"""
Gets the features of the Boston Housing Dataset. This is a 506x13 Matrix of Float64 datatypes.
The values are in the order ["crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat"].
It has 506 examples.
```jldoctest
julia> using Flux
julia> features = Flux.Data.Housing.features()
julia> summary(features)
506×13 Array{Float64,2}
julia> features[1, :]
13-element Array{Float64,1}:
0.00632
18.0
2.31
0.0
0.538
⋮
296.0
15.3
396.9
4.98
"""
function features()
load()
housing = readdlm(deps("housing.data"), ',')
Matrix{Float64}(housing[1:end, 1:13])
end
end

21
src/data/iris.jl
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@ -2,13 +2,12 @@
Fisher's classic iris dataset.
Measurements from 3 different species of iris: setosa, versicolor and
virginica. There are 50 examples of each species.
virginica. There are 50 examples of each species.
There are 4 measurements for each example: sepal length, sepal width, petal
length and petal width. The measurements are in centimeters.
There are 4 measurements for each example: sepal length, sepal width,
petal length and petal width. The measurements are in centimeters.
The module retrieves the data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).
"""
module Iris
@ -28,15 +27,12 @@ function load()
end
"""
labels()
Get the labels of the iris dataset, a 150 element array of strings listing the
species of each example.
```jldoctest
julia> using Flux
```jldoctest; setup = :(Flux.Data.Iris.load())
julia> labels = Flux.Data.Iris.labels();
julia> summary(labels)
@ -53,16 +49,13 @@ function labels()
end
"""
features()
Get the features of the iris dataset. This is a 4x150 matrix of Float64
elements. It has a row for each feature (sepal length, sepal width,
Get the features of the iris dataset. This is a 4x150 matrix of Float64
elements. It has a row for each feature (sepal length, sepal width,
petal length, petal width) and a column for each example.
```jldoctest
julia> using Flux
```jldoctest; setup = :(Flux.Data.Iris.load())
julia> features = Flux.Data.Iris.features();
julia> summary(features)

9
src/data/mnist.jl
View File

@ -83,9 +83,10 @@ getfeatures(io::IO, index::Integer) = vec(getimage(io, index))
Load the MNIST images.
Each image is a 28×28 array of `Gray` colour values (see Colors.jl).
Each image is a 28×28 array of `Gray` colour values
(see [Colors.jl](https://github.com/JuliaGraphics/Colors.jl)).
Returns the 60,000 training images by default; pass `:test` to retreive the
Return the 60,000 training images by default; pass `:test` to retrieve the
10,000 test images.
"""
function images(set = :train)
@ -99,10 +100,10 @@ end
labels()
labels(:test)
Load the labels corresponding to each of the images returned from `images()`.
Load the labels corresponding to each of the images returned from [`images()`](@ref).
Each label is a number from 0-9.
Returns the 60,000 training labels by default; pass `:test` to retreive the
Return the 60,000 training labels by default; pass `:test` to retrieve the
10,000 test labels.
"""
function labels(set = :train)

21
src/data/sentiment.jl
View File

@ -1,3 +1,4 @@
"Stanford Sentiment Treebank dataset."
module Sentiment
using ZipFile
@ -39,8 +40,28 @@ function gettrees(name)
return parsetree.(ss)
end
"""
train()
Return the train split of the Stanford Sentiment Treebank.
The data is in [treebank](https://en.wikipedia.org/wiki/Treebank) format.
"""
train() = gettrees("train")
"""
test()
Return the test split of the Stanford Sentiment Treebank.
The data is in [treebank](https://en.wikipedia.org/wiki/Treebank) format.
"""
test() = gettrees("test")
"""
dev()
Return the dev split of the Stanford Sentiment Treebank.
The data is in [treebank](https://en.wikipedia.org/wiki/Treebank) format.
"""
dev() = gettrees("dev")
end

32
src/functor.jl
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@ -39,6 +39,38 @@ end
trainable(m) = functor(m)[1]
"""
testmode!(m, mode = true)
Set a layer or model's test mode (see below).
Using `:auto` mode will treat any gradient computation as training.
_Note_: if you manually set a model into test mode, you need to manually place
it back into train mode during training phase.
Possible values include:
- `false` for training
- `true` for testing
- `:auto` or `nothing` for Flux to detect the mode automatically
"""
testmode!(m, mode = true) = m
"""
trainmode!(m, mode = true)
Set a layer of model's train mode (see below).
Symmetric to [`testmode!`](@ref) (i.e. `trainmode!(m, mode) == testmode!(m, !mode)).
_Note_: if you manually set a model into train mode, you need to manually place
it into test mode during testing phase.
Possible values include:
- `true` for training
- `false` for testing
- `:auto` or `nothing` for Flux to detect the mode automatically
"""
trainmode!(m, mode = true) = mode isa Bool ? testmode!(m, !mode) : testmode!(m, mode)
params!(p::Params, x::AbstractArray{<:Number}, seen = IdSet()) = push!(p, x)
function params!(p::Params, x, seen = IdSet())

87
src/layers/basic.jl
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@ -4,17 +4,23 @@
Chain multiple layers / functions together, so that they are called in sequence
on a given input.
```julia
m = Chain(x -> x^2, x -> x+1)
m(5) == 26
m = Chain(Dense(10, 5), Dense(5, 2))
x = rand(10)
m(x) == m[2](m[1](x))
```
`Chain` also supports indexing and slicing, e.g. `m[2]` or `m[1:end-1]`.
`m[1:3](x)` will calculate the output of the first three layers.
# Examples
```jldoctest
julia> m = Chain(x -> x^2, x -> x+1);
julia> m(5) == 26
true
julia> m = Chain(Dense(10, 5), Dense(5, 2));
julia> x = rand(10);
julia> m(x) == m[2](m[1](x))
true
```
"""
struct Chain{T<:Tuple}
layers::T
@ -33,6 +39,8 @@ applychain(fs::Tuple, x) = applychain(tail(fs), first(fs)(x))
Base.getindex(c::Chain, i::AbstractArray) = Chain(c.layers[i]...)
testmode!(m::Chain, mode = true) = (map(x -> testmode!(x, mode), m.layers); m)
function Base.show(io::IO, c::Chain)
print(io, "Chain(")
join(io, c.layers, ", ")
@ -58,6 +66,7 @@ outdims(c::Chain, isize) = foldl(∘, map(l -> (x -> outdims(l, x)), c.layers))(
# only slightly changed to better handle interaction with Zygote @dsweber2
"""
activations(c::Chain, input)
Calculate the forward results of each layers in Chain `c` with `input` as model input.
"""
function activations(c::Chain, input)
@ -76,22 +85,22 @@ extraChain(::Tuple{}, x) = ()
"""
Dense(in::Integer, out::Integer, σ = identity)
Creates a traditional `Dense` layer with parameters `W` and `b`.
Create a traditional `Dense` layer with parameters `W` and `b`.
y = σ.(W * x .+ b)
The input `x` must be a vector of length `in`, or a batch of vectors represented
as an `in × N` matrix. The out `y` will be a vector or batch of length `out`.
```julia
# Examples
```jldoctest; setup = :(using Random; Random.seed!(0))
julia> d = Dense(5, 2)
Dense(5, 2)
julia> d(rand(5))
Tracked 2-element Array{Float64,1}:
0.00257447
-0.00449443
```
2-element Array{Float32,1}:
-0.16210233
0.12311903```
"""
struct Dense{F,S,T}
W::S
@ -143,7 +152,7 @@ outdims(l::Dense, isize) = (size(l.W)[1],)
"""
Diagonal(in::Integer)
Creates an element-wise linear transformation layer with learnable
Create an element-wise linear transformation layer with learnable
vectors `α` and `β`:
y = α .* x .+ β
@ -174,18 +183,11 @@ outdims(l::Diagonal, isize) = (length(l.α),)
"""
Maxout(over)
`Maxout` is a neural network layer, which has a number of internal layers,
which all have the same input, and the maxout returns the elementwise maximium
of the internal layers' outputs.
The [Maxout](https://arxiv.org/pdf/1302.4389.pdf) layer has a number of
internal layers which all receive the same input. It returns the elementwise
maximum of the internal layers' outputs.
Maxout over linear dense layers satisfies the univeral approximation theorem.
Reference:
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio.
2013. Maxout networks.
In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML'13),
Sanjoy Dasgupta and David McAllester (Eds.), Vol. 28. JMLR.org III-1319-III-1327.
https://arxiv.org/pdf/1302.4389.pdf
"""
struct Maxout{FS<:Tuple}
over::FS
@ -194,17 +196,18 @@ end
"""
Maxout(f, n_alts)
Constructs a Maxout layer over `n_alts` instances of the layer given by `f`.
The function takes no arguement and should return some callable layer.
Conventionally this is a linear dense layer.
Construct a Maxout layer over `n_alts` instances of the layer given by `f`.
The function takes no arguments and should return some callable layer.
Conventionally, this is a linear dense layer.
For example the following example which
will construct a `Maxout` layer over 4 internal dense linear layers,
each identical in structure (784 inputs, 128 outputs).
# Examples
This constructs a `Maxout` layer over 4 internal dense linear layers, each
identical in structure (784 inputs, 128 outputs):
```julia
insize = 784
outsize = 128
Maxout(()->Dense(insize, outsize), 4)
insize = 784
outsize = 128
Maxout(()->Dense(insize, outsize), 4)
```
"""
function Maxout(f, n_alts)
@ -221,16 +224,18 @@ end
outdims(l::Maxout, isize) = outdims(first(l.over), isize)
"""
SkipConnection(layers, connection)
SkipConnection(layer, connection)
Creates a Skip Connection, of a layer or `Chain` of consecutive layers
plus a shortcut connection. The connection function will combine the result of the layers
with the original input, to give the final output.
Create a skip connection which consists of a layer or `Chain` of consecutive
layers and a shortcut connection linking the block's input to the output
through a user-supplied 2-argument callable. The first argument to the callable
will be propagated through the given `layer` while the second is the unchanged,
"skipped" input.
The simplest 'ResNet'-type connection is just `SkipConnection(layer, +)`,
The simplest "ResNet"-type connection is just `SkipConnection(layer, +)`,
and requires the output of the layers to be the same shape as the input.
Here is a more complicated example:
```
```julia
m = Conv((3,3), 4=>7, pad=(1,1))
x = ones(5,5,4,10);
size(m(x)) == (5, 5, 7, 10)

198
src/layers/conv.jl
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@ -9,20 +9,38 @@ expand(N, i::Tuple) = i
expand(N, i::Integer) = ntuple(_ -> i, N)
"""
Conv(filter::Tuple, in=>out)
Conv(filter::Tuple, in=>out, activation)
SamePad
Standard convolutional layer. `filter` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively.
Padding for convolutional layers will be calculated so that outputshape == inputshape when stride = 1.
Example: Applying Conv layer to a 1-channel input using a 2x2 window size,
giving us a 16-channel output. Output is activated with ReLU.
For stride > 1 the output shape depends on the type of convolution layer.
"""
struct SamePad end
calc_padding(pad, k::NTuple{N,T}, dilation, stride) where {T,N}= expand(Val(2*N), pad)
function calc_padding(::SamePad, k::NTuple{N,T}, dilation, stride) where {N,T}
#Ref: "A guide to convolution arithmetic for deep learning" https://arxiv.org/pdf/1603.07285
# Effective kernel size, including dilation
k_eff = @. k + (k - 1) * (dilation - 1)
# How much total padding needs to be applied?
pad_amt = @. k_eff - 1
# In case amount of padding is odd we need to apply different amounts to each side.
return Tuple(mapfoldl(i -> [ceil(Int, i/2), floor(Int, i/2)], vcat, pad_amt))
end
"""
Conv(filter, in => out, σ = identity; init = glorot_uniform,
stride = 1, pad = 0, dilation = 1)
filter = (2,2)
in = 1
out = 16
Conv((2, 2), 1=>16, relu)
Standard convolutional layer. `filter` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively.
Data should be stored in WHCN order (width, height, # channels, batch size).
In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array.
@ -31,6 +49,18 @@ Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride.
# Examples
Apply a `Conv` layer to a 1-channel input using a 2×2 window filter size, giving us a
16-channel output. Output is activated with ReLU.
```julia
filter = (2,2)
in = 1
out = 16
Conv(filter, in => out, relu)
```
"""
struct Conv{N,M,F,A,V}
σ::F
@ -46,18 +76,27 @@ end
Conv(weight::AbstractArray, bias::AbstractArray, activation)
Constructs the convolutional layer with user defined weight and bias arrays.
All other behaviours of the Conv layer apply with regard to data order and
forward pass.
Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
There is also a keyword-only constuctor available for all convoultional
layers.
```julia
weight = rand(Float32, 3, 3, 5)
bias = zeros(Float32, 5)
Conv(weight = weight,
bias = bias,
σ = sigmoid)
```
"""
function Conv(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation)
pad = calc_padding(pad, size(w)[1:N-2], dilation, stride)
return Conv(σ, w, b, stride, pad, dilation)
end
@ -114,8 +153,8 @@ end
"""
outdims(l::Conv, isize::Tuple)
Calculate the output dimensions given the input dimensions, `isize`.
Batch size and channel size are ignored as per `NNlib.jl`.
Calculate the output dimensions given the input dimensions `isize`.
Batch size and channel size are ignored as per [NNlib.jl](https://github.com/FluxML/NNlib.jl).
```julia
m = Conv((3, 3), 3 => 16)
@ -127,19 +166,23 @@ outdims(l::Conv, isize) =
output_size(DenseConvDims(_paddims(isize, size(l.weight)), size(l.weight); stride = l.stride, padding = l.pad, dilation = l.dilation))
"""
ConvTranspose(size, in=>out)
ConvTranspose(size, in=>out, activation)
ConvTranspose(filter, in=>out)
ConvTranspose(filter, in=>out, activation)
ConvTranspose(filter, in => out, σ = identity; init = glorot_uniform,
stride = 1, pad = 0, dilation = 1)
Standard convolutional transpose layer. `filter` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively.
Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Data should be stored in WHCN order (width, height, # channels, batch size).
In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
Use `pad=SamePad()` to apply padding so that outputsize == stride * inputsize - stride + 1.
"""
struct ConvTranspose{N,M,F,A,V}
σ::F
@ -155,18 +198,19 @@ end
ConvTranspose(weight::AbstractArray, bias::AbstractArray, activation)
Constructs the convolutional transpose layer with user defined weight and bias arrays.
All other behaviours of the ConvTranspose layer apply with regard to data order and
forward pass.
Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
For keyword-only constuctor, see also [`Conv`](@ref)
"""
function ConvTranspose(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation)
pad = calc_padding(pad, size(w)[1:N-2], dilation, stride)
return ConvTranspose(σ, w, b, stride, pad, dilation)
end
@ -227,18 +271,22 @@ outdims(l::ConvTranspose{N}, isize) where N = _convtransoutdims(isize[1:2], size
"""
DepthwiseConv(filter::Tuple, in=>out)
DepthwiseConv(filter::Tuple, in=>out, activation)
DepthwiseConv(filter, in => out, σ = identity; init = glorot_uniform,
stride = 1, pad = 0, dilation = 1)
Depthwise convolutional layer. `filter` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively.
Note that `out` must be an integer multiple of `in`.
Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Data should be stored in WHCN order (width, height, # channels, batch size).
In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride.
"""
struct DepthwiseConv{N,M,F,A,V}
σ::F
@ -254,18 +302,19 @@ end
DepthwiseConv(weight::AbstractArray, bias::AbstractArray, activation)
Constructs the `DepthwiseConv` layer with user defined weight and bias arrays.
All other behaviours of the `DepthwiseConv` layer apply with regard to data order and
forward pass.
Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
For keyword-only constuctor, see also [`Conv`](@ref)
"""
function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation)
pad = calc_padding(pad, size(w)[1:N-2], dilation, stride)
return DepthwiseConv(σ, w, b, stride, pad, dilation)
end
@ -328,21 +377,15 @@ outdims(l::DepthwiseConv, isize) =
output_size(DepthwiseConvDims(_paddims(isize, (1, 1, size(l.weight)[end], 1)), size(l.weight); stride = l.stride, padding = l.pad, dilation = l.dilation))
"""
CrossCor(size, in=>out)
CrossCor(size, in=>out, activation)
CrossCor(filter, in=>out)
CrossCor(filter, in=>out, activation)
CrossCor(filter, in => out, σ = identity; init = glorot_uniform,
stride = 1, pad = 0, dilation = 1)
Standard cross convolutional layer. `size` should be a tuple like `(2, 2)`.
Standard cross convolutional layer. `filter` should be a tuple like `(2, 2)`.
`in` and `out` specify the number of input and output channels respectively.
Example: Applying CrossCor layer to a 1-channel input using a 2x2 window size,
giving us a 16-channel output. Output is activated with ReLU.
size = (2,2)
in = 1
out = 16
CrossCor((2, 2), 1=>16, relu)
Data should be stored in WHCN order (width, height, # channels, # batches).
Data should be stored in WHCN order (width, height, # channels, batch size).
In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array.
@ -350,6 +393,18 @@ Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride.
# Examples
Apply a `CrossCor` layer to a 1-channel input using a 2×2 window filter size, giving us a
16-channel output. Output is activated with ReLU.
```julia
filter = (2,2)
in = 1
out = 16
CrossCor((2, 2), 1=>16, relu)
```
"""
struct CrossCor{N,M,F,A,V}
σ::F
@ -365,18 +420,19 @@ end
CrossCor(weight::AbstractArray, bias::AbstractArray, activation)
Constructs the standard cross convolutional layer with user defined weight and bias
arrays. All other behaviours of the CrossCor layer apply with regard to data order and
forward pass.
arrays.
Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
For keyword-only constuctor, see also [`Conv`](@ref)
"""
function CrossCor(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation)
pad = calc_padding(pad, size(w)[1:N-2], dilation, stride)
return CrossCor(σ, w, b, stride, pad, dilation)
end
@ -425,11 +481,62 @@ outdims(l::CrossCor, isize) =
output_size(DenseConvDims(_paddims(isize, size(l.weight)), size(l.weight); stride = l.stride, padding = l.pad, dilation = l.dilation))
"""
MaxPool(k)
GlobalMaxPool()
Max pooling layer. `k` stands for the size of the window for each dimension of the input.
Global max pooling layer.
Takes the keyword arguments `pad` and `stride`.
Transforms (w,h,c,b)-shaped input into (1,1,c,b)-shaped output,
by performing max pooling on the complete (w,h)-shaped feature maps.
"""
struct GlobalMaxPool end
function (g::GlobalMaxPool)(x)
# Input size
x_size = size(x)
# Kernel size
k = x_size[1:end-2]
# Pooling dimensions
pdims = PoolDims(x, k)
return maxpool(x, pdims)
end
function Base.show(io::IO, g::GlobalMaxPool)
print(io, "GlobalMaxPool()")
end
"""
GlobalMeanPool()
Global mean pooling layer.
Transforms (w,h,c,b)-shaped input into (1,1,c,b)-shaped output,
by performing mean pooling on the complete (w,h)-shaped feature maps.
"""
struct GlobalMeanPool end
function (g::GlobalMeanPool)(x)
# Input size
x_size = size(x)
# Kernel size
k = x_size[1:end-2]
# Pooling dimensions
pdims = PoolDims(x, k)
return meanpool(x, pdims)
end
function Base.show(io::IO, g::GlobalMeanPool)
print(io, "GlobalMeanPool()")
end
"""
MaxPool(k; pad = 0, stride = k)
Max pooling layer. `k` is the size of the window for each dimension of the input.
Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride.
=======
"""
struct MaxPool{N,M}
k::NTuple{N,Int}
@ -439,8 +546,7 @@ end
function MaxPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N
stride = expand(Val(N), stride)
pad = expand(Val(2*N), pad)
pad = calc_padding(pad, k, 1, stride)
return MaxPool(k, pad, stride)
end
@ -456,11 +562,11 @@ end
outdims(l::MaxPool{N}, isize) where N = output_size(PoolDims(_paddims(isize, (l.k..., 1, 1)), l.k; stride = l.stride, padding = l.pad))
"""
MeanPool(k)
MeanPool(k; pad = 0, stride = k)
Mean pooling layer. `k` stands for the size of the window for each dimension of the input.
Mean pooling layer. `k` is the size of the window for each dimension of the input.
Takes the keyword arguments `pad` and `stride`.
Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride.
"""
struct MeanPool{N,M}
k::NTuple{N,Int}
@ -470,7 +576,7 @@ end
function MeanPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N
stride = expand(Val(N), stride)
pad = expand(Val(2*N), pad)
pad = calc_padding(pad, k, 1, stride)
return MeanPool(k, pad, stride)
end
@ -483,4 +589,4 @@ function Base.show(io::IO, m::MeanPool)
print(io, "MeanPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
end
outdims(l::MeanPool{N}, isize) where N = output_size(PoolDims(_paddims(isize, (l.k..., 1, 1)), l.k; stride = l.stride, padding = l.pad))
outdims(l::MeanPool{N}, isize) where N = output_size(PoolDims(_paddims(isize, (l.k..., 1, 1)), l.k; stride = l.stride, padding = l.pad))

185
src/layers/normalise.jl
View File

@ -2,11 +2,23 @@ istraining() = false
@adjoint istraining() = true, _ -> nothing
_isactive(m) = isnothing(m.active) ? istraining() : m.active
_dropout_shape(s, ::Colon) = size(s)
_dropout_shape(s, dims) = tuple((i ∉ dims ? 1 : si for (i, si) ∈ enumerate(size(s)))...)
_dropout_kernel(y::T, p, q) where {T} = y > p ? T(1 / q) : T(0)
"""
dropout(x, p; dims = :)
The dropout function. For each input, either sets that input to `0` (with probability
`p`) or scales it by `1 / (1 - p)`. `dims` specifies the unbroadcasted dimensions,
e.g. `dims=1` applies dropout along columns and `dims=2` along rows.
This is used as a regularisation, i.e. it reduces overfitting during training.
See also the [`Dropout`](@ref) layer.
"""
dropout(x, p; dims = :) = x
@adjoint function dropout(x, p; dims = :)
@ -18,22 +30,31 @@ end
"""
Dropout(p, dims = :)
A Dropout layer. For each input, either sets that input to `0` (with probability
`p`) or scales it by `1/(1-p)`. The `dims` argument is to specified the unbroadcasted
dimensions, i.e. `dims=1` does dropout along columns and `dims=2` along rows. This is
used as a regularisation, i.e. it reduces overfitting during training. see also [`dropout`](@ref).
Dropout layer. In the forward pass, apply the [`Flux.dropout`](@ref) function on the input.
Does nothing to the input once [`Flux.testmode!`](@ref) is `true`.
"""
mutable struct Dropout{F,D}
p::F
dims::D
active::Union{Bool, Nothing}
end
# TODO: deprecate in v0.11
Dropout(p, dims) = Dropout(p, dims, nothing)
function Dropout(p; dims = :)
@assert 0 ≤ p ≤ 1
Dropout{typeof(p),typeof(dims)}(p, dims)
Dropout{typeof(p),typeof(dims)}(p, dims, nothing)
end
(a::Dropout)(x) = dropout(x, a.p; dims = a.dims)
function (a::Dropout)(x)
_isactive(a) || return x
return dropout(x, a.p; dims = a.dims)
end
testmode!(m::Dropout, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
function Base.show(io::IO, d::Dropout)
print(io, "Dropout(", d.p)
@ -43,20 +64,25 @@ end
"""
AlphaDropout(p)
A dropout layer. It is used in Self-Normalizing Neural Networks.
(https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf)
The AlphaDropout layer ensures that mean and variance of activations remains the same as before.
A dropout layer. Used in
[Self-Normalizing Neural Networks](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf).
The AlphaDropout layer ensures that mean and variance of activations
remain the same as before.
Does nothing to the input once [`testmode!`](@ref) is true.
"""
mutable struct AlphaDropout{F}
p::F
function AlphaDropout(p)
active::Union{Bool, Nothing}
function AlphaDropout(p, active = nothing)
@assert 0 ≤ p ≤ 1
new{typeof(p)}(p)
new{typeof(p)}(p, active)
end
end
function (a::AlphaDropout)(x)
istraining() || return x
_isactive(a) || return x
λ = eltype(x)(1.0507009873554804934193349852946)
α = eltype(x)(1.6732632423543772848170429916717)
α1 = eltype(x)(-λ*α)
@ -68,12 +94,15 @@ function (a::AlphaDropout)(x)
return x
end
testmode!(m::AlphaDropout, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
"""
LayerNorm(h::Integer)
A [normalisation layer](https://arxiv.org/pdf/1607.06450.pdf) designed to be
used with recurrent hidden states of size `h`. Normalises the mean/stddev of
each input before applying a per-neuron gain/bias.
used with recurrent hidden states of size `h`. Normalises the mean and standard
deviation of each input before applying a per-neuron gain/bias.
"""
struct LayerNorm{T}
diag::Diagonal{T}
@ -95,8 +124,8 @@ end
initβ = zeros, initγ = ones,
ϵ = 1e-8, momentum = .1)
Batch Normalization layer. The `channels` input should be the size of the
channel dimension in your data (see below).
[Batch Normalization](https://arxiv.org/pdf/1502.03167.pdf) layer.
`channels` should be the size of the channel dimension in your data (see below).
Given an array with `N` dimensions, call the `N-1`th the channel dimension. (For
a batch of feature vectors this is just the data dimension, for `WHCN` images
@ -106,10 +135,9 @@ it's the usual channel dimension.)
shifts them to have a new mean and variance (corresponding to the learnable,
per-channel `bias` and `scale` parameters).
See [Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf).
Use [`testmode!`](@ref) during inference.
Example:
# Examples
```julia
m = Chain(
Dense(28^2, 64),
@ -127,12 +155,16 @@ mutable struct BatchNorm{F,V,W,N}
σ²::W # moving std
ϵ::N
momentum::N
active::Union{Bool, Nothing}
end
# TODO: deprecate in v0.11
BatchNorm(λ, β, γ, μ, σ², ϵ, momentum) = BatchNorm(λ, β, γ, μ, σ², ϵ, momentum, nothing)
BatchNorm(chs::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
BatchNorm(λ, initβ(chs), initγ(chs),
zeros(chs), ones(chs), ϵ, momentum)
zeros(chs), ones(chs), ϵ, momentum, nothing)
trainable(bn::BatchNorm) = (bn.β, bn.γ)
@ -145,7 +177,7 @@ function (BN::BatchNorm)(x)
m = div(prod(size(x)), channels)
γ = reshape(BN.γ, affine_shape...)
β = reshape(BN.β, affine_shape...)
if !istraining()
if !_isactive(BN)
μ = reshape(BN.μ, affine_shape...)
σ² = reshape(BN.σ², affine_shape...)
ϵ = BN.ϵ
@ -170,41 +202,15 @@ end
@functor BatchNorm
testmode!(m::BatchNorm, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
function Base.show(io::IO, l::BatchNorm)
print(io, "BatchNorm($(join(size(l.β), ", "))")
(l.λ == identity) || print(io, ", λ = $(l.λ)")
print(io, ")")
end
"""
InstanceNorm(channels::Integer, σ = identity;
initβ = zeros, initγ = ones,
ϵ = 1e-8, momentum = .1)
Instance Normalization layer. The `channels` input should be the size of the
channel dimension in your data (see below).
Given an array with `N` dimensions, call the `N-1`th the channel dimension. (For
a batch of feature vectors this is just the data dimension, for `WHCN` images
it's the usual channel dimension.)
`InstanceNorm` computes the mean and variance for each each `W×H×1×1` slice and
shifts them to have a new mean and variance (corresponding to the learnable,
per-channel `bias` and `scale` parameters).
See [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022).
Example:
```julia
m = Chain(
Dense(28^2, 64),
InstanceNorm(64, relu),
Dense(64, 10),
InstanceNorm(10),
softmax)
```
"""
expand_inst = (x, as) -> reshape(repeat(x, outer=[1, as[length(as)]]), as...)
mutable struct InstanceNorm{F,V,W,N}
@ -215,12 +221,44 @@ mutable struct InstanceNorm{F,V,W,N}
σ²::W # moving std
ϵ::N
momentum::N
active::Union{Bool, Nothing}
end
# TODO: deprecate in v0.11
"""
InstanceNorm(channels::Integer, σ = identity;
initβ = zeros, initγ = ones,
ϵ = 1e-8, momentum = .1)
[Instance Normalization](https://arxiv.org/abs/1607.08022) layer.
`channels` should be the size of the channel dimension in your data (see below).
Given an array with `N` dimensions, call the `N-1`th the channel dimension. (For
a batch of feature vectors this is just the data dimension, for `WHCN` images
it's the usual channel dimension.)
`InstanceNorm` computes the mean and variance for each each `W×H×1×1` slice and
shifts them to have a new mean and variance (corresponding to the learnable,
per-channel `bias` and `scale` parameters).
Use [`testmode!`](@ref) during inference.
# Examples
```julia
m = Chain(
Dense(28^2, 64),
InstanceNorm(64, relu),
Dense(64, 10),
InstanceNorm(10),
softmax)
```
"""
InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum) = InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum, nothing)
InstanceNorm(chs::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
InstanceNorm(λ, initβ(chs), initγ(chs),
zeros(chs), ones(chs), ϵ, momentum)
zeros(chs), ones(chs), ϵ, momentum, nothing)
trainable(in::InstanceNorm) = (in.β, in.γ)
@ -237,7 +275,7 @@ function (in::InstanceNorm)(x)
m = div(prod(size(x)), c*bs)
γ, β = expand_inst(in.γ, affine_shape), expand_inst(in.β, affine_shape)
if !istraining()
if !_isactive(in)
μ = expand_inst(in.μ, affine_shape)
σ² = expand_inst(in.σ², affine_shape)
ϵ = in.ϵ
@ -263,6 +301,9 @@ end
@functor InstanceNorm
testmode!(m::InstanceNorm, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
function Base.show(io::IO, l::InstanceNorm)
print(io, "InstanceNorm($(join(size(l.β), ", "))")
(l.λ == identity) || print(io, ", λ = $(l.λ)")
@ -270,26 +311,27 @@ function Base.show(io::IO, l::InstanceNorm)
end
"""
Group Normalization.
This layer can outperform Batch-Normalization and Instance-Normalization.
GroupNorm(chs::Integer, G::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i),
ϵ = 1f-5, momentum = 0.1f0)
GroupNorm(chs::Integer, G::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i),
ϵ = 1f-5, momentum = 0.1f0)
[Group Normalization](https://arxiv.org/pdf/1803.08494.pdf) layer.
This layer can outperform Batch Normalization and Instance Normalization.
``chs`` is the number of channels, the channel dimension of your input.
For an array of N dimensions, the (N-1)th index is the channel dimension.
`chs` is the number of channels, the channel dimension of your input.
For an array of N dimensions, the `N-1`th index is the channel dimension.
``G`` is the number of groups along which the statistics would be computed.
`G` is the number of groups along which the statistics are computed.
The number of channels must be an integer multiple of the number of groups.
Example:
```
m = Chain(Conv((3,3), 1=>32, leakyrelu;pad = 1),
GroupNorm(32,16)) # 32 channels, 16 groups (G = 16), thus 2 channels per group used
```
Use [`testmode!`](@ref) during inference.
Link : https://arxiv.org/pdf/1803.08494.pdf
# Examples
```julia
m = Chain(Conv((3,3), 1=>32, leakyrelu;pad = 1),
GroupNorm(32,16))
# 32 channels, 16 groups (G = 16), thus 2 channels per group used
```
"""
mutable struct GroupNorm{F,V,W,N,T}
G::T # number of groups
@ -300,12 +342,16 @@ mutable struct GroupNorm{F,V,W,N,T}
σ²::W # moving std
ϵ::N
momentum::N
active::Union{Bool, Nothing}
end
# TODO: deprecate in v0.11
GroupNorm(G, λ, β, γ, μ, σ², ϵ, momentum) = GroupNorm(G, λ, β, γ, μ, σ², ϵ, momentum, nothing)
GroupNorm(chs::Integer, G::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
GroupNorm(G, λ, initβ(chs), initγ(chs),
zeros(G,1), ones(G,1), ϵ, momentum)
zeros(G,1), ones(G,1), ϵ, momentum, nothing)
trainable(gn::GroupNorm) = (gn.β, gn.γ)
@ -329,7 +375,7 @@ function(gn::GroupNorm)(x)
β = reshape(gn.β, affine_shape...)
y = reshape(x,((size(x))[1:end-2]...,channels_per_group,groups,batches))
if !istraining()
if !_isactive(gn)
og_shape = size(x)
μ = reshape(gn.μ, μ_affine_shape...) # Shape : (1,1,...C/G,G,1)
σ² = reshape(gn.σ², μ_affine_shape...) # Shape : (1,1,...C/G,G,1)
@ -360,6 +406,9 @@ end
@functor GroupNorm
testmode!(m::GroupNorm, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
function Base.show(io::IO, l::GroupNorm)
print(io, "GroupNorm($(join(size(l.β), ", "))")
(l.λ == identity) || print(io, ", λ = $(l.λ)")

29
src/layers/recurrent.jl
View File

@ -12,16 +12,16 @@ in the background. `cell` should be a model of the form:
h, y = cell(h, x...)
For example, here's a recurrent network that keeps a running total of its inputs.
For example, here's a recurrent network that keeps a running total of its inputs:
```julia
accum(h, x) = (h+x, x)
accum(h, x) = (h + x, x)
rnn = Flux.Recur(accum, 0)
rnn(2) # 2
rnn(3) # 3
rnn.state # 5
rnn.(1:10) # apply to a sequence
rnn.state # 60
rnn(2) # 2
rnn(3) # 3
rnn.state # 5
rnn.(1:10) # apply to a sequence
rnn.state # 60
```
"""
mutable struct Recur{T}
@ -47,9 +47,10 @@ Base.show(io::IO, m::Recur) = print(io, "Recur(", m.cell, ")")
Reset the hidden state of a recurrent layer back to its original value.
Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to
rnn.state = hidden(rnn.cell)
Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to:
```julia
rnn.state = hidden(rnn.cell)
```
"""
reset!(m::Recur) = (m.state = m.init)
reset!(m) = foreach(reset!, functor(m)[1])
@ -135,8 +136,8 @@ Base.show(io::IO, l::LSTMCell) =
"""
LSTM(in::Integer, out::Integer)
Long Short Term Memory recurrent layer. Behaves like an RNN but generally
exhibits a longer memory span over sequences.
[Long Short Term Memory](https://www.researchgate.net/publication/13853244_Long_Short-term_Memory)
recurrent layer. Behaves like an RNN but generally exhibits a longer memory span over sequences.
See [this article](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
for a good overview of the internals.
@ -176,8 +177,8 @@ Base.show(io::IO, l::GRUCell) =
"""
GRU(in::Integer, out::Integer)
Gated Recurrent Unit layer. Behaves like an RNN but generally
exhibits a longer memory span over sequences.
[Gated Recurrent Unit](https://arxiv.org/abs/1406.1078) layer. Behaves like an
RNN but generally exhibits a longer memory span over sequences.
See [this article](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
for a good overview of the internals.

248
src/layers/stateless.jl
View File

@ -1,10 +1,58 @@
using CuArrays
using NNlib: logsoftmax, logσ
# Cost functions
"""
mae(ŷ, y)
Return the mean of absolute error; calculated as
`sum(abs.(ŷ .- y)) / length(y)`.
"""
mae(ŷ, y) = sum(abs.(ŷ .- y)) * 1 // length(y)
"""
mse(ŷ, y)
Return the mean squared error between ŷ and y; calculated as
`sum((ŷ .- y).^2) / length(y)`.
# Examples
```jldoctest
julia> Flux.mse([0, 2], [1, 1])
1//1
```
"""
mse(ŷ, y) = sum((ŷ .- y).^2) * 1 // length(y)
"""
msle(ŷ, y; ϵ=eps(eltype(ŷ)))
Return the mean of the squared logarithmic errors; calculated as
`sum((log.(ŷ .+ ϵ) .- log.(y .+ ϵ)).^2) / length(y)`.
The `ϵ` term provides numerical stability.
Penalizes an under-predicted estimate greater than an over-predicted estimate.
"""
msle(ŷ, y; ϵ=eps(eltype(ŷ))) = sum((log.(ŷ .+ ϵ) .- log.(y .+ ϵ)).^2) * 1 // length(y)
"""
huber_loss(ŷ, y; δ=1.0)
Return the mean of the [Huber loss](https://en.wikipedia.org/wiki/Huber_loss)
given the prediction `ŷ` and true values `y`.
| 0.5 * |ŷ - y|, for |ŷ - y| <= δ
Huber loss = |
| δ * (|ŷ - y| - 0.5 * δ), otherwise
"""
function huber_loss(ŷ, y; δ=eltype(ŷ)(1))
abs_error = abs.(ŷ .- y)
temp = abs_error .< δ
x = eltype(ŷ)(0.5)
hub_loss = sum(((abs_error.^2) .* temp) .* x .+ δ*(abs_error .- x*δ) .* (1 .- temp)) * 1 // length(y)
end
function _crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, weight::Nothing)
return -sum(y .* log.(ŷ)) * 1 // size(y, 2)
end
@ -17,22 +65,63 @@ function _crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, weight::Abstr
return -sum(y .* log.(ŷ) .* weight) * 1 // size(y, 2)
end
"""
crossentropy(ŷ, y; weight = nothing)
Return the cross entropy between the given probability distributions;
calculated as `-sum(y .* log.(ŷ) .* weight) / size(y, 2)`.
`weight` can be `Nothing`, a `Number` or an `AbstractVector`.
`weight=nothing` acts like `weight=1` but is faster.
See also: [`Flux.logitcrossentropy`](@ref), [`Flux.binarycrossentropy`](@ref), [`Flux.logitbinarycrossentropy`](@ref)
# Examples
```jldoctest
julia> Flux.crossentropy(softmax([-1.1491, 0.8619, 0.3127]), [1, 1, 0])
3.085467254747739
```
"""
crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight=nothing) = _crossentropy(ŷ, y, weight)
function logitcrossentropy(logŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight = 1)
return -sum(y .* logsoftmax(logŷ) .* weight) * 1 // size(y, 2)
"""
logitcrossentropy(ŷ, y; weight = 1)
Return the crossentropy computed after a [`Flux.logsoftmax`](@ref) operation;
calculated as `-sum(y .* logsoftmax(ŷ) .* weight) / size(y, 2)`.
`logitcrossentropy(ŷ, y)` is mathematically equivalent to
[`Flux.crossentropy(softmax(log(ŷ)), y)`](@ref) but it is more numerically stable.
See also: [`Flux.crossentropy`](@ref), [`Flux.binarycrossentropy`](@ref), [`Flux.logitbinarycrossentropy`](@ref)
# Examples
```jldoctest
julia> Flux.logitcrossentropy([-1.1491, 0.8619, 0.3127], [1, 1, 0])
3.085467254747738
```
"""
function logitcrossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight = 1)
return -sum(y .* logsoftmax(ŷ) .* weight) * 1 // size(y, 2)
end
"""
binarycrossentropy(ŷ, y; ϵ=eps(ŷ))
Return `-y*log(ŷ + ϵ) - (1-y)*log(1-ŷ + ϵ)`. The ϵ term provides numerical stability.
Return ``-y*\\log(ŷ + ϵ) - (1-y)*\\log(1-ŷ + ϵ)``. The `ϵ` term provides numerical stability.
julia> binarycrossentropy.(σ.([-1.1491, 0.8619, 0.3127]), [1, 1, 0.])
3-element Array{Float64,1}:
1.4244
0.352317
0.86167
Typically, the prediction `ŷ` is given by the output of a [`sigmoid`](@ref) activation.
See also: [`Flux.crossentropy`](@ref), [`Flux.logitcrossentropy`](@ref), [`Flux.logitbinarycrossentropy`](@ref)
# Examples
```jldoctest
julia> Flux.binarycrossentropy.(σ.([-1.1491, 0.8619, 0.3127]), [1, 1, 0])
3-element Array{Float64,1}:
1.424397097347566
0.35231664672364077
0.8616703662235441
```
"""
binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -y*log(ŷ + ϵ) - (1 - y)*log(1 - ŷ + ϵ)
@ -40,44 +129,52 @@ binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -y*log(ŷ + ϵ) - (1 - y)*log(1 - ŷ
CuArrays.@cufunc binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -y*log(ŷ + ϵ) - (1 - y)*log(1 - ŷ + ϵ)
"""
logitbinarycrossentropy(logŷ, y)
logitbinarycrossentropy(ŷ, y)
`logitbinarycrossentropy(logŷ, y)` is mathematically equivalent to `binarycrossentropy(σ(logŷ), y)`
but it is more numerically stable.
`logitbinarycrossentropy(ŷ, y)` is mathematically equivalent to
[`Flux.binarycrossentropy(σ(log(ŷ)), y)`](@ref) but it is more numerically stable.
julia> logitbinarycrossentropy.([-1.1491, 0.8619, 0.3127], [1, 1, 0.])
3-element Array{Float64,1}:
1.4244
0.352317
0.86167
See also: [`Flux.crossentropy`](@ref), [`Flux.logitcrossentropy`](@ref), [`Flux.binarycrossentropy`](@ref)
# Examples
```jldoctest
julia> Flux.logitbinarycrossentropy.([-1.1491, 0.8619, 0.3127], [1, 1, 0])
3-element Array{Float64,1}:
1.4243970973475661
0.35231664672364094
0.8616703662235443
```
"""
logitbinarycrossentropy(logŷ, y) = (1 - y)*logŷ - logσ(logŷ)
logitbinarycrossentropy(ŷ, y) = (1 - y)*ŷ - logσ(ŷ)
# Re-definition to fix interaction with CuArrays.
CuArrays.@cufunc logitbinarycrossentropy(logŷ, y) = (1 - y)*logŷ - logσ(logŷ)
CuArrays.@cufunc logitbinarycrossentropy(ŷ, y) = (1 - y)*ŷ - logσ(ŷ)
"""
normalise(x::AbstractArray; dims=1)
normalise(x; dims=1)
Normalises `x` to mean 0 and standard deviation 1, across the dimensions given by `dims`. Defaults to normalising over columns.
Normalise `x` to mean 0 and standard deviation 1 across the dimensions given by `dims`.
Defaults to normalising over columns.
julia> a = reshape(collect(1:9), 3, 3)
3×3 Array{Int64,2}:
1 4 7
2 5 8
3 6 9
```jldoctest
julia> a = reshape(collect(1:9), 3, 3)
3×3 Array{Int64,2}:
1 4 7
2 5 8
3 6 9
julia> normalise(a)
3×3 Array{Float64,2}:
-1.22474 -1.22474 -1.22474
0.0 0.0 0.0
1.22474 1.22474 1.22474
julia> Flux.normalise(a)
3×3 Array{Float64,2}:
-1.22474 -1.22474 -1.22474
0.0 0.0 0.0
1.22474 1.22474 1.22474
julia> normalise(a, dims=2)
3×3 Array{Float64,2}:
-1.22474 0.0 1.22474
-1.22474 0.0 1.22474
-1.22474 0.0 1.22474
julia> Flux.normalise(a, dims=2)
3×3 Array{Float64,2}:
-1.22474 0.0 1.22474
-1.22474 0.0 1.22474
-1.22474 0.0 1.22474
```
"""
function normalise(x::AbstractArray; dims=1)
μ′ = mean(x, dims = dims)
@ -87,26 +184,81 @@ end
"""
kldivergence(ŷ, y)
KLDivergence is a measure of how much one probability distribution is different from the other.
It is always non-negative and zero only when both the distributions are equal everywhere.
[KL Divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence).
Return the
[Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)
between the given probability distributions.
KL divergence is a measure of how much one probability distribution is different
from the other.
It is always non-negative and zero only when both the distributions are equal
everywhere.
"""
function kldivergence(ŷ, y)
entropy = sum(y .* log.(y)) *1 //size(y,2)
entropy = sum(y .* log.(y)) * 1 //size(y,2)
cross_entropy = crossentropy(ŷ, y)
return entropy + cross_entropy
end
"""
poisson(ŷ, y)
Poisson loss function is a measure of how the predicted distribution diverges from the expected distribution.
[Poisson Loss](https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/poisson).
Return how much the predicted distribution `ŷ` diverges from the expected Poisson
distribution `y`; calculated as `sum(ŷ .- y .* log.(ŷ)) / size(y, 2)`.
[More information.](https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/poisson).
"""
poisson(ŷ, y) = sum(ŷ .- y .* log.(ŷ)) *1 // size(y,2)
poisson(ŷ, y) = sum(ŷ .- y .* log.(ŷ)) * 1 // size(y,2)
"""
hinge(ŷ, y)
Measures the loss given the prediction ŷ and true labels y(containing 1 or -1).
[Hinge Loss](https://en.wikipedia.org/wiki/Hinge_loss).
Return the [hinge loss](https://en.wikipedia.org/wiki/Hinge_loss) given the
prediction `ŷ` and true labels `y` (containing 1 or -1); calculated as
`sum(max.(0, 1 .- ŷ .* y)) / size(y, 2)`.
See also: [`squared_hinge`](@ref)
"""
hinge(ŷ, y) = sum(max.(0, 1 .- ŷ .* y)) *1 // size(y,2)
hinge(ŷ, y) = sum(max.(0, 1 .- ŷ .* y)) * 1 // size(y, 2)
"""
squared_hinge(ŷ, y)
Return the squared hinge loss given the prediction `ŷ` and true labels `y`
(containing 1 or -1); calculated as `sum((max.(0, 1 .- ŷ .* y)).^2) / size(y, 2)`.
See also: [`hinge`](@ref)
"""
squared_hinge(ŷ, y) = sum((max.(0, 1 .- ŷ .* y)).^2) * 1 // size(y, 2)
"""
dice_coeff_loss(ŷ, y; smooth=1)
Return a loss based on the dice coefficient.
Used in the [V-Net](https://arxiv.org/pdf/1606.04797v1.pdf) image segmentation
architecture.
Similar to the F1_score. Calculated as:
1 - 2*sum(|ŷ .* y| + smooth) / (sum(ŷ.^2) + sum(y.^2) + smooth)`
"""
dice_coeff_loss(ŷ, y; smooth=eltype(ŷ)(1.0)) = 1 - (2*sum(y .* ŷ) + smooth) / (sum(y.^2) + sum(ŷ.^2) + smooth)
"""
tversky_loss(ŷ, y; β=0.7)
Return the [Tversky loss](https://arxiv.org/pdf/1706.05721.pdf).
Used with imbalanced data to give more weight to false negatives.
Larger β weigh recall higher than precision (by placing more emphasis on false negatives)
Calculated as:
1 - sum(|y .* ŷ| + 1) / (sum(y .* ŷ + β*(1 .- y) .* ŷ + (1 - β)*y .* (1 .- ŷ)) + 1)
"""
tversky_loss(ŷ, y; β=eltype(ŷ)(0.7)) = 1 - (sum(y .* ŷ) + 1) / (sum(y .* ŷ + β*(1 .- y) .* ŷ + (1 - β)*y .* (1 .- ŷ)) + 1)
"""
flatten(x::AbstractArray)
Transform (w, h, c, b)-shaped input into (w × h × c, b)-shaped output
by linearizing all values for each element in the batch.
"""
function flatten(x::AbstractArray)
return reshape(x, :, size(x)[end])
end

40
src/onehot.jl
View File

@ -37,30 +37,28 @@ import Adapt: adapt, adapt_structure
adapt_structure(T, xs::OneHotMatrix) = OneHotMatrix(xs.height, adapt(T, xs.data))
import .CuArrays: CuArray, cudaconvert
import .CuArrays: CuArray, CuArrayStyle, cudaconvert
import Base.Broadcast: BroadcastStyle, ArrayStyle
BroadcastStyle(::Type{<:OneHotMatrix{<:CuArray}}) = ArrayStyle{CuArray}()
BroadcastStyle(::Type{<:OneHotMatrix{<:CuArray}}) = CuArrayStyle{2}()
cudaconvert(x::OneHotMatrix{<:CuArray}) = OneHotMatrix(x.height, cudaconvert(x.data))
"""
onehot(l, labels[, unk])
Create an [`OneHotVector`](@ref) wtih `l`-th element be `true` based on possible `labels` set.
If `unk` is given, it retruns `onehot(unk, labels)` if the input label `l` is not find in `labels`; otherwise
it will error.
## Examples
Create a `OneHotVector` with its `l`-th element `true` based on the
possible set of `labels`.
If `unk` is given, return `onehot(unk, labels)` if the input label `l` is not found
in `labels`; otherwise it will error.
# Examples
```jldoctest
julia> using Flux: onehot
julia> onehot(:b, [:a, :b, :c])
julia> Flux.onehot(:b, [:a, :b, :c])
3-element Flux.OneHotVector:
0
1
0
julia> onehot(:c, [:a, :b, :c])
julia> Flux.onehot(:c, [:a, :b, :c])
3-element Flux.OneHotVector:
0
0
@ -82,15 +80,14 @@ end
"""
onehotbatch(ls, labels[, unk...])
Create an [`OneHotMatrix`](@ref) with a batch of labels based on possible `labels` set, returns the
`onehot(unk, labels)` if given labels `ls` is not found in set `labels`.
## Examples
Create a `OneHotMatrix` with a batch of labels based on the
possible set of `labels`.
If `unk` is given, return [`onehot(unk, labels)`](@ref) if one of the input
labels `ls` is not found in `labels`; otherwise it will error.
# Examples
```jldoctest
julia> using Flux: onehotbatch
julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
julia> Flux.onehotbatch([:b, :a, :b], [:a, :b, :c])
3×3 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:
0 1 0
1 0 1
@ -107,13 +104,12 @@ Base.argmax(xs::OneHotVector) = xs.ix
Inverse operations of [`onehot`](@ref).
# Examples
```jldoctest
julia> using Flux: onecold
julia> onecold([true, false, false], [:a, :b, :c])
julia> Flux.onecold([true, false, false], [:a, :b, :c])
:a
julia> onecold([0.3, 0.2, 0.5], [:a, :b, :c])
julia> Flux.onecold([0.3, 0.2, 0.5], [:a, :b, :c])
:c
```
"""

4
src/optimise/Optimise.jl
View File

@ -1,7 +1,7 @@
module Optimise
export train!,
SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
export train!, update!,
Descent, ADAM, Momentum, Nesterov, RMSProp,
ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM, ADAMW,RADAM,
InvDecay, ExpDecay, WeightDecay, stop, Optimiser

275
src/optimise/optimisers.jl
View File

@ -6,24 +6,25 @@ const ϵ = 1e-8
# TODO: should use weak refs
"""
Descent(η)
Descent(η = 0.1)
Classic gradient descent optimiser with learning rate `η`.
For each parameter `p` and its gradient `δp`, this runs `p -= η*δp`
## Parameters
- Learning Rate (η): The amount by which the gradients are discounted before updating the weights. Defaults to `0.1`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
## Example
```julia-repl
opt = Descent() # uses default η (0.1)
# Examples
```julia
opt = Descent()
opt = Descent(0.3) # use provided η
opt = Descent(0.3)
ps = params(model)
gs = gradient(ps) do
loss(x, y)
loss(x, y)
end
Flux.Optimise.update!(opt, ps, gs)
@ -40,17 +41,19 @@ function apply!(o::Descent, x, Δ)
end
"""
Momentum(η, ρ)
Momentum(η = 0.01, ρ = 0.9)
Gradient descent with learning rate `η` and momentum `ρ`.
Gradient descent optimizer with learning rate `η` and momentum `ρ`.
## Parameters
- Learning Rate (`η`): Amount by which gradients are discounted before updating the weights. Defaults to `0.01`.
- Momentum (`ρ`): Parameter that accelerates descent in the relevant direction and dampens oscillations. Defaults to `0.9`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Momentum (`ρ`): Controls the acceleration of gradient descent in the
prominent direction, in effect dampening oscillations.
## Examples
# Examples
```julia
opt = Momentum() # uses defaults of η = 0.01 and ρ = 0.9
opt = Momentum()
opt = Momentum(0.01, 0.99)
```
@ -71,17 +74,19 @@ function apply!(o::Momentum, x, Δ)
end
"""
Nesterov(η, ρ)
Nesterov(η = 0.001, ρ = 0.9)
Gradient descent with learning rate `η` and Nesterov momentum `ρ`.
Gradient descent optimizer with learning rate `η` and Nesterov momentum `ρ`.
## Parameters
- Learning Rate (η): Amount by which the gradients are dicsounted berfore updating the weights. Defaults to `0.001`.
- Nesterov Momentum (ρ): Paramters controlling the amount of nesterov momentum to be applied. Defaults to `0.9`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Nesterov momentum (`ρ`): Controls the acceleration of gradient descent in the
prominent direction, in effect dampening oscillations.
## Examples
# Examples
```julia
opt = Nesterov() # uses defaults η = 0.001 and ρ = 0.9
opt = Nesterov()
opt = Nesterov(0.003, 0.95)
```
@ -103,23 +108,25 @@ function apply!(o::Nesterov, x, Δ)
end
"""
RMSProp(η, ρ)
RMSProp(η = 0.001, ρ = 0.9)
Implements the RMSProp algortihm. Often a good choice for recurrent networks. Paramters other than learning rate generally don't need tuning.
Optimizer using the
[RMSProp](https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
algorithm. Often a good choice for recurrent networks. Parameters other than learning rate
generally don't need tuning.
## Parameters
- Learning Rate (η): Defaults to `0.001`.
- Rho (ρ): Defaults to `0.9`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Momentum (`ρ`): Controls the acceleration of gradient descent in the
prominent direction, in effect dampening oscillations.
## Examples
# Examples
```julia
opt = RMSProp() # uses default η = 0.001 and ρ = 0.9
opt = RMSProp()
opt = RMSProp(0.002, 0.95)
```
## References
[RMSProp](https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
"""
mutable struct RMSProp
eta::Float64
@ -137,23 +144,22 @@ function apply!(o::RMSProp, x, Δ)
end
"""
ADAM(η, β::Tuple)
ADAM(η = 0.001, β::Tuple = (0.9, 0.999))
Implements the ADAM optimiser.
[ADAM](https://arxiv.org/abs/1412.6980v8) optimiser.
## Paramters
- Learning Rate (`η`): Defaults to `0.001`.
- Beta (`β::Tuple`): The first element refers to β1 and the second to β2. Defaults to `(0.9, 0.999)`.
## Examples
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Decay of momentums (`β::Tuple`): Exponential decay for the first (β1) and the
second (β2) momentum estimate.
# Examples
```julia
opt = ADAM() # uses the default η = 0.001 and β = (0.9, 0.999)
opt = ADAM()
opt = ADAM(0.001, (0.9, 0.8))
```
## References
[ADAM](https://arxiv.org/abs/1412.6980v8) optimiser.
"""
mutable struct ADAM
eta::Float64
@ -174,24 +180,22 @@ function apply!(o::ADAM, x, Δ)
end
"""
RADAM(η, β::Tuple)
RADAM(η = 0.001, β::Tuple = (0.9, 0.999))
Implements the rectified ADAM optimizer.
[Rectified ADAM](https://arxiv.org/pdf/1908.03265v1.pdf) optimizer.
## Parameters
- Learning Rate (η): Defaults to `0.001`
- Beta (β::Tuple): The first element refers to β1 and the second to β2. Defaults to `(0.9, 0.999)`.
## Examples
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Decay of momentums (`β::Tuple`): Exponential decay for the first (β1) and the
second (β2) momentum estimate.
# Examples
```julia
opt = RADAM() # uses the default η = 0.001 and β = (0.9, 0.999)
opt = RADAM()
opt = RADAM(0.001, (0.9, 0.8))
```
## References
[RADAM](https://arxiv.org/pdf/1908.03265v1.pdf) optimiser (Rectified ADAM).
"""
mutable struct RADAM
eta::Float64
@ -219,22 +223,22 @@ function apply!(o::RADAM, x, Δ)
end
"""
AdaMax(η, β::Tuple)
AdaMax(η = 0.001, β::Tuple = (0.9, 0.999))
Variant of ADAM based on ∞-norm.
[AdaMax](https://arxiv.org/abs/1412.6980v9) is a variant of ADAM based on the ∞-norm.
## Parameters
- Learning Rate (η): Defaults to `0.001`
- Beta (β::Tuple): The first element refers to β1 and the second to β2. Defaults to `(0.9, 0.999)`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Decay of momentums (`β::Tuple`): Exponential decay for the first (β1) and the
second (β2) momentum estimate.
## Examples
# Examples
```julia
opt = AdaMax() # uses default η and β
opt = AdaMax()
opt = AdaMax(0.001, (0.9, 0.995))
```
## References
[AdaMax](https://arxiv.org/abs/1412.6980v9) optimiser.
"""
mutable struct AdaMax
eta::Float64
@ -255,23 +259,22 @@ function apply!(o::AdaMax, x, Δ)
end
"""
ADAGrad(η)
ADAGrad(η = 0.1)
Implements AdaGrad. It has parameter specific learning rates based on how frequently it is updated.
[ADAGrad](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) optimizer. It has
parameter specific learning rates based on how frequently it is updated.
Parameters don't need tuning.
## Parameters
- Learning Rate (η): Defaults to `0.1`
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
## Examples
# Examples
```julia
opt = ADAGrad() # uses default η = 0.1
opt = ADAGrad()
opt = ADAGrad(0.001)
```
## References
[ADAGrad](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) optimiser.
Parameters don't need tuning.
"""
mutable struct ADAGrad
eta::Float64
@ -288,21 +291,21 @@ function apply!(o::ADAGrad, x, Δ)
end
"""
ADADelta(ρ)
ADADelta(ρ = 0.9)
Version of ADAGrad that adapts learning rate based on a window of past gradient updates. Parameters don't need tuning.
[ADADelta](https://arxiv.org/abs/1212.5701) is a version of ADAGrad adapting its learning
rate based on a window of past gradient updates.
Parameters don't need tuning.
## Parameters
- Rho (ρ): Factor by which gradient is decayed at each time step. Defaults to `0.9`.
# Parameters
- Rho (`ρ`): Factor by which the gradient is decayed at each time step.
## Examples
# Examples
```julia
opt = ADADelta() # uses default ρ = 0.9
opt = ADADelta()
opt = ADADelta(0.89)
```
## References
[ADADelta](https://arxiv.org/abs/1212.5701) optimiser.
"""
mutable struct ADADelta
rho::Float64
@ -321,22 +324,23 @@ function apply!(o::ADADelta, x, Δ)
end
"""
AMSGrad(η, β::Tuple)
AMSGrad(η = 0.001, β::Tuple = (0.9, 0.999))
Implements AMSGrad version of the ADAM optimiser. Parameters don't need tuning.
The [AMSGrad](https://openreview.net/forum?id=ryQu7f-RZ) version of the ADAM
optimiser. Parameters don't need tuning.
## Parameters
- Learning Rate (η): Defaults to `0.001`.
- Beta (β::Tuple): The first element refers to β1 and the second to β2. Defaults to `(0.9, 0.999)`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Decay of momentums (`β::Tuple`): Exponential decay for the first (β1) and the
second (β2) momentum estimate.
## Examples
# Examples
```julia
opt = AMSGrad() # uses default η and β
opt = AMSGrad()
opt = AMSGrad(0.001, (0.89, 0.995))
```
## References
[AMSGrad](https://openreview.net/forum?id=ryQu7f-RZ) optimiser.
"""
mutable struct AMSGrad
eta::Float64
@ -356,22 +360,23 @@ function apply!(o::AMSGrad, x, Δ)
end
"""
NADAM(η, β::Tuple)
NADAM(η = 0.001, β::Tuple = (0.9, 0.999))
Nesterov variant of ADAM. Parameters don't need tuning.
[NADAM](http://cs229.stanford.edu/proj2015/054_report.pdf) is a Nesterov variant of ADAM.
Parameters don't need tuning.
## Parameters
- Learning Rate (η): Defaults to `0.001`.
- Beta (β::Tuple): The first element refers to β1 and the second to β2. Defaults to `(0.9, 0.999)`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Decay of momentums (`β::Tuple`): Exponential decay for the first (β1) and the
second (β2) momentum estimate.
## Examples
# Examples
```julia
opt = NADAM() # uses default η and β
opt = NADAM()
opt = NADAM(0.002, (0.89, 0.995))
```
## References
[NADAM](http://cs229.stanford.edu/proj2015/054_report.pdf) optimiser.
"""
mutable struct NADAM
eta::Float64
@ -392,23 +397,24 @@ function apply!(o::NADAM, x, Δ)
end
"""
ADAMW(η, β::Tuple, decay)
ADAMW(η = 0.001, β::Tuple = (0.9, 0.999), decay = 0)
Variant of ADAM defined by fixing weight decay regularization.
[ADAMW](https://arxiv.org/abs/1711.05101) is a variant of ADAM fixing (as in repairing) its
weight decay regularization.
## Parameters
- Learning Rate (η): Defaults to `0.001`.
- Beta (β::Tuple): The first element refers to β1 and the second to β2. Defaults to (0.9, 0.999).
- decay: Decay applied to weights during optimisation. Defaults to 0.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- Decay of momentums (`β::Tuple`): Exponential decay for the first (β1) and the
second (β2) momentum estimate.
- `decay`: Decay applied to weights during optimisation.
## Examples
# Examples
```julia
opt = ADAMW() # uses default η, β and decay
opt = ADAMW()
opt = ADAMW(0.001, (0.89, 0.995), 0.1)
```
## References
[ADAMW](https://arxiv.org/abs/1711.05101)
"""
ADAMW(η = 0.001, β = (0.9, 0.999), decay = 0) =
Optimiser(ADAM(η, β), WeightDecay(decay))
@ -441,17 +447,15 @@ function apply!(o::Optimiser, x, Δ)
end
"""
InvDecay(γ)
InvDecay(γ = 0.001)
Applies inverse time decay to an optimiser, i.e., the effective step size at iteration `n` is `eta / (1 + γ * n)` where `eta` is the initial step size. The wrapped optimiser's step size is not modified.
```
Apply inverse time decay to an optimiser, so that the effective step size at
iteration `n` is `eta / (1 + γ * n)` where `eta` is the initial step size.
The wrapped optimiser's step size is not modified.
## Parameters
- gamma (γ): Defaults to `0.001`
## Example
# Examples
```julia
Optimiser(InvDecay(..), Opt(..))
Optimiser(InvDecay(..), Opt(..))
```
"""
mutable struct InvDecay
@ -470,22 +474,25 @@ function apply!(o::InvDecay, x, Δ)
end
"""
ExpDecay(eta, decay, decay_step, clip)
ExpDecay(η = 0.001, decay = 0.1, decay_step = 1000, clip = 1e-4)
Discount the learning rate `eta` by a multiplicative factor `decay` every `decay_step` till a minimum of `clip`.
Discount the learning rate `η` by the factor `decay` every `decay_step` steps till
a minimum of `clip`.
## Parameters
- Learning Rate (eta): Defaults to `0.001`.
- decay: Factor by which the learning rate is discounted. Defaults to `0.1`.
- decay_step: Schedules decay operations by setting number of steps between two decay operations. Defaults to `1000`.
- clip: Minimum value of learning rate. Defaults to `1e-4`.
# Parameters
- Learning rate (`η`): Amount by which gradients are discounted before updating
the weights.
- `decay`: Factor by which the learning rate is discounted.
- `decay_step`: Schedule decay operations by setting the number of steps between
two decay operations.
- `clip`: Minimum value of learning rate.
## Example
# Examples
To apply exponential decay to an optimiser:
```julia
Optimiser(ExpDecay(..), Opt(..))
Optimiser(ExpDecay(..), Opt(..))
opt = Optimiser(ExpDecay(), ADAM())
opt = Optimiser(ExpDecay(), ADAM())
```
"""
mutable struct ExpDecay
@ -509,12 +516,12 @@ function apply!(o::ExpDecay, x, Δ)
end
"""
WeightDecay(wd)
WeightDecay(wd = 0)
Decays the weight by `wd`
Decay weights by `wd`.
## Parameters
- weight decay (wd): 0
# Parameters
- Weight decay (`wd`)
"""
mutable struct WeightDecay
wd::Real

62
src/optimise/train.jl
View File

@ -1,11 +1,26 @@
using Juno
import Zygote: Params, gradient
"""
update!(x, x̄)
Update the array `x` according to `x .-= x̄`.
"""
function update!(x::AbstractArray, x̄)
x .+= x̄
return x
x .-= x̄
end
"""
update!(opt, p, g)
update!(opt, ps::Params, gs)
Perform an update step of the parameters `ps` (or the single parameter `p`)
according to optimizer `opt` and the gradients `gs` (the gradient `g`).
As a result, the parameters are mutated and the optimizer's internal state may change.
"""
function update!(opt, x, x̄)
x .-= apply!(opt, x, x̄)
end
@ -28,11 +43,10 @@ struct StopException <: Exception end
stop()
Call `Flux.stop()` in a callback to indicate when a callback condition is met.
This would trigger the train loop to stop and exit.
This will trigger the train loop to stop and exit.
# Examples
```julia
# Example callback:
cb = function ()
accuracy() > 0.9 && Flux.stop()
end
@ -45,18 +59,19 @@ end
"""
train!(loss, params, data, opt; cb)
For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
backpropagation and calls the optimizer `opt`.
For each datapoint `d` in `data` compute the gradient of `loss(d...)` through
backpropagation and call the optimizer `opt`.
Takes a callback as keyword argument `cb`. For example, this will print "training"
every 10 seconds:
In case datapoints `d` are of numeric array type, assume no splatting is needed
and compute the gradient of `loss(d)`.
```julia
Flux.train!(loss, params, data, opt,
cb = throttle(() -> println("training"), 10))
```
A callback is given with the keyword argument `cb`. For example, this will print
"training" every 10 seconds (using [`Flux.throttle`](@ref)):
The callback can call `Flux.stop()` to interrupt the training loop.
train!(loss, params, data, opt,
cb = throttle(() -> println("training"), 10))
The callback can call [`Flux.stop`](@ref) to interrupt the training loop.
Multiple optimisers and callbacks can be passed to `opt` and `cb` as arrays.
"""
@ -65,8 +80,14 @@ function train!(loss, ps, data, opt; cb = () -> ())
cb = runall(cb)
@progress for d in data
try
gs = gradient(ps) do
loss(d...)
if d isa AbstractArray{<:Number}
gs = gradient(ps) do
loss(d)
end
else
gs = gradient(ps) do
loss(d...)
end
end
update!(opt, ps, gs)
cb()
@ -86,11 +107,12 @@ end
Run `body` `N` times. Mainly useful for quickly doing multiple epochs of
training in a REPL.
```julia
julia> @epochs 2 println("hello")
INFO: Epoch 1
# Examples
```jldoctest
julia> Flux.@epochs 2 println("hello")
[ Info: Epoch 1
hello
INFO: Epoch 2
[ Info: Epoch 2
hello
```
"""

175
src/utils.jl
View File

@ -1,10 +1,40 @@
# Arrays
nfan() = 1, 1 #fan_in, fan_out
nfan(n) = 1, n #A vector is treated as a n×1 matrix
nfan(n_out, n_in) = n_in, n_out #In case of Dense kernels: arranged as matrices
nfan(dims...) = prod(dims[1:end-2]) .* (dims[end-1], dims[end]) #In case of convolution kernels
nfan() = 1, 1 # fan_in, fan_out
nfan(n) = 1, n # A vector is treated as a n×1 matrix
nfan(n_out, n_in) = n_in, n_out # In case of Dense kernels: arranged as matrices
nfan(dims...) = prod(dims[1:end-2]) .* (dims[end-1], dims[end]) # In case of convolution kernels
"""
glorot_uniform(dims...)
Return an `Array` of size `dims` containing random variables taken from a uniform
distribution in the interval ``[-x, x]``, where `x = sqrt(24 / sum(dims)) / 2`.
# Examples
```jldoctest; setup = :(using Random; Random.seed!(0))
julia> Flux.glorot_uniform(2, 3)
2×3 Array{Float32,2}:
0.601094 -0.57414 -0.814925
0.900868 0.805994 0.057514
```
"""
glorot_uniform(dims...) = (rand(Float32, dims...) .- 0.5f0) .* sqrt(24.0f0 / sum(nfan(dims...)))
"""
glorot_normal(dims...)
Return an `Array` of size `dims` containing random variables taken from a normal
distribution with mean 0 and standard deviation `sqrt(2 / sum(dims))`.
# Examples
```jldoctest; setup = :(using Random; Random.seed!(0))
julia> Flux.glorot_normal(3, 2)
3×2 Array{Float32,2}:
0.429505 -0.0852891
0.523935 0.371009
-0.223261 0.188052
```
"""
glorot_normal(dims...) = randn(Float32, dims...) .* sqrt(2.0f0 / sum(nfan(dims...)))
ones(T::Type, dims...) = Base.ones(T, dims...)
@ -13,9 +43,81 @@ zeros(T::Type, dims...) = Base.zeros(T, dims...)
ones(dims...) = Base.ones(Float32, dims...)
zeros(dims...) = Base.zeros(Float32, dims...)
"""
unsqueeze(xs, dim)
Return `xs` reshaped into an `Array` one dimensionality higher than `xs`,
where `dim` indicates in which dimension `xs` is extended.
# Examples
```jldoctest
julia> xs = [[1, 2], [3, 4], [5, 6]]
3-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
[5, 6]
julia> Flux.unsqueeze(xs, 1)
1×3 Array{Array{Int64,1},2}:
[1, 2] [3, 4] [5, 6]
julia> Flux.unsqueeze([1 2; 3 4], 2)
2×1×2 Array{Int64,3}:
[:, :, 1] =
1
3
[:, :, 2] =
2
4
```
"""
unsqueeze(xs, dim) = reshape(xs, (size(xs)[1:dim-1]..., 1, size(xs)[dim:end]...))
"""
stack(xs, dim)
Concatenate the given `Array` of `Array`s `xs` into a single `Array` along the
given dimension `dim`.
# Examples
```jldoctest
julia> xs = [[1, 2], [3, 4], [5, 6]]
3-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
[5, 6]
julia> Flux.stack(xs, 1)
3×2 Array{Int64,2}:
1 2
3 4
5 6
julia> cat(xs, dims=1)
3-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
[5, 6]
```
"""
stack(xs, dim) = cat(unsqueeze.(xs, dim)..., dims=dim)
"""
unstack(xs, dim)
Unroll the given `xs` into an `Array` of `Array`s along the given dimension `dim`.
# Examples
```jldoctest
julia> Flux.unstack([1 3 5 7; 2 4 6 8], 2)
4-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
[5, 6]
[7, 8]
```
"""
unstack(xs, dim) = [copy(selectdim(xs, dim, i)) for i in 1:size(xs, dim)]
"""
@ -23,9 +125,16 @@ unstack(xs, dim) = [copy(selectdim(xs, dim, i)) for i in 1:size(xs, dim)]
Split `xs` into `n` parts.
```julia
julia> chunk(1:10, 3)
3-element Array{Array{Int64,1},1}:
# Examples
```jldoctest
julia> Flux.chunk(1:10, 3)
3-element Array{UnitRange{Int64},1}:
1:4
5:8
9:10
julia> Flux.chunk(collect(1:10), 3)
3-element Array{SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true},1}:
[1, 2, 3, 4]
[5, 6, 7, 8]
[9, 10]
@ -40,11 +149,12 @@ batchindex(xs, i) = (reverse(Base.tail(reverse(axes(xs))))..., i)
Count the number of times that each element of `xs` appears.
```julia
julia> frequencies(['a','b','b'])
# Examples
```jldoctest
julia> Flux.frequencies(['a','b','b'])
Dict{Char,Int64} with 2 entries:
'b' => 2
'a' => 1
'b' => 2
```
"""
function frequencies(xs)
@ -60,12 +170,13 @@ head(x::Tuple) = reverse(Base.tail(reverse(x)))
squeezebatch(x) = reshape(x, head(size(x)))
"""
batch(xs)
batch(xs)
Batch the arrays in `xs` into a single array.
```julia
julia> batch([[1,2,3],[4,5,6]])
# Examples
```jldoctest
julia> Flux.batch([[1,2,3],[4,5,6]])
3×2 Array{Int64,2}:
1 4
2 5
@ -82,6 +193,25 @@ function batch(xs)
return data
end
"""
Return the given sequence padded with `p` up to a maximum length of `n`.
# Examples
```jldoctest
julia> rpad([1, 2], 4, 0)
4-element Array{Int64,1}:
1
2
0
0
julia> rpad([1, 2, 3], 2, 0)
3-element Array{Int64,1}:
1
2
3
```
"""
Base.rpad(v::AbstractVector, n::Integer, p) = [v; fill(p, max(n - length(v), 0))]
"""
@ -90,8 +220,9 @@ Base.rpad(v::AbstractVector, n::Integer, p) = [v; fill(p, max(n - length(v), 0))
Take a list of `N` sequences, and turn them into a single sequence where each
item is a batch of `N`. Short sequences will be padded by `pad`.
```julia
julia> batchseq([[1, 2, 3], [4, 5]], 0)
# Examples
```jldoctest
julia> Flux.batchseq([[1, 2, 3], [4, 5]], 0)
3-element Array{Array{Int64,1},1}:
[1, 4]
[2, 5]
@ -148,11 +279,15 @@ end
# Other
"""
Returns a function that when invoked, will only be triggered at most once
during `timeout` seconds. Normally, the throttled function will run
as much as it can, without ever going more than once per `wait` duration;
but if you'd like to disable the execution on the leading edge, pass
`leading=false`. To enable execution on the trailing edge, ditto.
throttle(f, timeout; leading=true, trailing=false)
Return a function that when invoked, will only be triggered at most once
during `timeout` seconds.
Normally, the throttled function will run as much as it can, without ever
going more than once per `wait` duration; but if you'd like to disable the
execution on the leading edge, pass `leading=false`. To enable execution on
the trailing edge, pass `trailing=true`.
"""
function throttle(f, timeout; leading=true, trailing=false)
cooldown = true

7
test/cuda/cuda.jl
View File

@ -58,6 +58,13 @@ end
@test y[3,:] isa CuArray
end
@testset "restructure gpu" begin
dudt = Dense(1,1) |> gpu
p,re = Flux.destructure(dudt)
foo(x) = sum(re(p)(x))
@test gradient(foo, cu(rand(1)))[1] isa CuArray
end
if CuArrays.has_cudnn()
@info "Testing Flux/CUDNN"
include("cudnn.jl")

92
test/data.jl
View File

@ -1,22 +1,86 @@
using Flux.Data
using Test
@testset "DataLoader" begin
X = reshape([1:10;], (2, 5))
Y = [1:5;]
@test cmudict()["CATASTROPHE"] == :[K,AH0,T,AE1,S,T,R,AH0,F,IY0].args
d = DataLoader(X, batchsize=2)
batches = collect(d)
@test length(batches) == 3
@test batches[1] == X[:,1:2]
@test batches[2] == X[:,3:4]
@test batches[3] == X[:,5:5]
@test length(CMUDict.phones()) == 39
d = DataLoader(X, batchsize=2, partial=false)
batches = collect(d)
@test length(batches) == 2
@test batches[1] == X[:,1:2]
@test batches[2] == X[:,3:4]
@test length(CMUDict.symbols()) == 84
d = DataLoader(X, Y, batchsize=2)
batches = collect(d)
@test length(batches) == 3
@test length(batches[1]) == 2
@test length(batches[2]) == 2
@test length(batches[3]) == 2
@test batches[1][1] == X[:,1:2]
@test batches[1][2] == Y[1:2]
@test batches[2][1] == X[:,3:4]
@test batches[2][2] == Y[3:4]
@test batches[3][1] == X[:,5:5]
@test batches[3][2] == Y[5:5]
@test MNIST.images()[1] isa Matrix
@test MNIST.labels() isa Vector{Int64}
# test interaction with `train!`
θ = ones(2)
X = zeros(2, 10)
loss(x) = sum((x .- θ).^2)
d = DataLoader(X)
Flux.train!(loss, [θ], ncycle(d, 10), Descent(0.1))
@test norm(θ) < 1e-4
@test FashionMNIST.images()[1] isa Matrix
@test FashionMNIST.labels() isa Vector{Int64}
# test interaction with `train!`
θ = zeros(2)
X = ones(2, 10)
Y = fill(2, 10)
loss(x, y) = sum((y - x'*θ).^2)
d = DataLoader(X, Y)
Flux.train!(loss, [θ], ncycle(d, 10), Descent(0.1))
@test norm(θ .- 1) < 1e-10
end
@test Data.Sentiment.train() isa Vector{Data.Tree{Any}}
@testset "CMUDict" begin
@test cmudict()["CATASTROPHE"] == :[K,AH0,T,AE1,S,T,R,AH0,F,IY0].args
@test Iris.features() isa Matrix
@test size(Iris.features()) == (4,150)
@test length(CMUDict.phones()) == 39
@test Iris.labels() isa Vector{String}
@test size(Iris.labels()) == (150,)
@test length(CMUDict.symbols()) == 84
end
@testset "MNIST" begin
@test MNIST.images()[1] isa Matrix
@test MNIST.labels() isa Vector{Int64}
end
@testset "FashionMNIST" begin
@test FashionMNIST.images()[1] isa Matrix
@test FashionMNIST.labels() isa Vector{Int64}
end
@testset "Sentiment" begin
@test Data.Sentiment.train() isa Vector{Data.Tree{Any}}
end
@testset "Iris" begin
@test Iris.features() isa Matrix
@test size(Iris.features()) == (4,150)
@test Iris.labels() isa Vector{String}
@test size(Iris.labels()) == (150,)
end
@testset "Housing" begin
@test Housing.features() isa Matrix # test broken due to SSL certifate expiration problem
@test size(Housing.features()) == (506, 13)
@test Housing.targets() isa Array{Float64}
@test size(Housing.targets()) == (506, 1)
end

30
test/layers/conv.jl
View File

@ -4,6 +4,10 @@ using Flux: gradient
@testset "Pooling" begin
x = randn(Float32, 10, 10, 3, 2)
gmp = GlobalMaxPool()
@test size(gmp(x)) == (1, 1, 3, 2)
gmp = GlobalMeanPool()
@test size(gmp(x)) == (1, 1, 3, 2)
mp = MaxPool((2, 2))
@test mp(x) == maxpool(x, PoolDims(x, 2))
mp = MeanPool((2, 2))
@ -187,4 +191,28 @@ end
@test Flux.outdims(m, (5, 5)) == (4, 4)
m = MeanPool((2, 2); stride = 2, pad = 3)
@test Flux.outdims(m, (5, 5)) == (5, 5)
end
end
@testset "$ltype SamePad kernelsize $k" for ltype in (Conv, ConvTranspose, DepthwiseConv, CrossCor), k in ( (1,), (2,), (3,), (4,5), (6,7,8))
data = ones(Float32, (k .+ 3)..., 1,1)
l = ltype(k, 1=>1, pad=SamePad())
@test size(l(data)) == size(data)
l = ltype(k, 1=>1, pad=SamePad(), dilation = k .÷ 2)
@test size(l(data)) == size(data)
stride = 3
l = ltype(k, 1=>1, pad=SamePad(), stride = stride)
if ltype == ConvTranspose
@test size(l(data))[1:end-2] == stride .* size(data)[1:end-2] .- stride .+ 1
else
@test size(l(data))[1:end-2] == ceil.(Int, size(data)[1:end-2] ./ stride)
end
end
@testset "$ltype SamePad windowsize $k" for ltype in (MeanPool, MaxPool), k in ( (1,), (2,), (3,), (4,5), (6,7,8))
data = ones(Float32, (k .+ 3)..., 1,1)
l = ltype(k, pad=SamePad())
@test size(l(data))[1:end-2] == ceil.(Int, size(data)[1:end-2] ./ k)
end

46
test/layers/normalisation.jl
View File

@ -1,30 +1,32 @@
using Flux, Test, Statistics
using Zygote: pullback
trainmode(f, x...) = pullback(f, x...)[1]
trainmode(f) = (x...) -> trainmode(f, x...)
evalwgrad(f, x...) = pullback(f, x...)[1]
@testset "Dropout" begin
x = [1.,2.,3.]
@test x == Dropout(0.1)(x)
@test x == trainmode(Dropout(0), x)
@test zero(x) == trainmode(Dropout(1), x)
@test x == evalwgrad(Dropout(0), x)
@test zero(x) == evalwgrad(Dropout(1), x)
x = rand(100)
m = Dropout(0.9)
y = trainmode(m, x)
y = evalwgrad(m, x)
@test count(a->a==0, y) > 50
y = m(x)
testmode!(m, true)
y = evalwgrad(m, x) # should override istraining
@test count(a->a==0, y) == 0
y = trainmode(m, x)
testmode!(m, false)
y = evalwgrad(m, x)
@test count(a->a==0, y) > 50
x = rand(Float32, 100)
m = Chain(Dense(100,100),
Dropout(0.9))
y = trainmode(m, x)
y = evalwgrad(m, x)
@test count(a->a == 0, y) > 50
y = m(x)
testmode!(m, true)
y = evalwgrad(m, x) # should override istraining
@test count(a->a == 0, y) == 0
x = rand(100, 50)
@ -49,7 +51,7 @@ end
# initial m.σ is 1
# initial m.μ is 0
y = trainmode(m, x)
y = evalwgrad(m, x)
@test isapprox(y, [-1.22474 0 1.22474; -1.22474 0 1.22474], atol = 1.0e-5)
# julia> x
# 2×3 Array{Float64,2}:
@ -82,19 +84,19 @@ end
@test isapprox(y, sigmoid.((x .- m.μ) ./ sqrt.(m.σ² .+ m.ϵ)), atol = 1.0e-7)
end
let m = trainmode(BatchNorm(2)), x = reshape(Float32.(1:6), 3, 2, 1)
let m = trainmode!(BatchNorm(2)), x = reshape(Float32.(1:6), 3, 2, 1)
y = reshape(permutedims(x, [2, 1, 3]), 2, :)
y = permutedims(reshape(m(y), 2, 3, 1), [2, 1, 3])
@test m(x) == y
end
let m = trainmode(BatchNorm(2)), x = reshape(Float32.(1:12), 2, 3, 2, 1)
let m = trainmode!(BatchNorm(2)), x = reshape(Float32.(1:12), 2, 3, 2, 1)
y = reshape(permutedims(x, [3, 1, 2, 4]), 2, :)
y = permutedims(reshape(m(y), 2, 2, 3, 1), [2, 3, 1, 4])
@test m(x) == y
end
let m = trainmode(BatchNorm(2)), x = reshape(Float32.(1:24), 2, 2, 3, 2, 1)
let m = trainmode!(BatchNorm(2)), x = reshape(Float32.(1:24), 2, 2, 3, 2, 1)
y = reshape(permutedims(x, [4, 1, 2, 3, 5]), 2, :)
y = permutedims(reshape(m(y), 2, 2, 2, 3, 1), [2, 3, 4, 1, 5])
@test m(x) == y
@ -117,7 +119,7 @@ end
x = Float64.(x)
@test m.β == [0, 0] # initβ(2)
@test m.γ == [1, 1] # initγ(2)
y = trainmode(m, x)
y = evalwgrad(m, x)
#julia> x
#[:, :, 1] =
@ -162,7 +164,7 @@ end
@test isapprox(y, sigmoid.((x .- expand_inst(m.μ, affine_shape)) ./ sqrt.(expand_inst(m.σ², affine_shape) .+ m.ϵ)), atol = 1.0e-7)
end
let m = trainmode(InstanceNorm(2)), sizes = (2, 4, 1, 2, 3),
let m = trainmode!(InstanceNorm(2)), sizes = (2, 4, 1, 2, 3),
x = Float32.(reshape(collect(1:prod(sizes)), sizes))
y = reshape(permutedims(x, [3, 1, 2, 4, 5]), :, 2, 3)
y = reshape(m(y), sizes...)
@ -172,14 +174,14 @@ end
# check that μ, σ², and the output are the correct size for higher rank tensors
let m = InstanceNorm(2), sizes = (5, 5, 3, 4, 2, 6),
x = reshape(Float32.(collect(1:prod(sizes))), sizes)
y = trainmode(m, x)
y = evalwgrad(m, x)
@test size(m.μ) == (sizes[end - 1], )
@test size(m.σ²) == (sizes[end - 1], )
@test size(y) == sizes
end
# show that instance norm is equal to batch norm when channel and batch dims are squashed
let m_inorm = trainmode(InstanceNorm(2)), m_bnorm = trainmode(BatchNorm(12)), sizes = (5, 5, 3, 4, 2, 6),
let m_inorm = trainmode!(InstanceNorm(2)), m_bnorm = trainmode!(BatchNorm(12)), sizes = (5, 5, 3, 4, 2, 6),
x = reshape(Float32.(collect(1:prod(sizes))), sizes)
@test m_inorm(x) == reshape(m_bnorm(reshape(x, (sizes[1:end - 2]..., :, 1))), sizes)
end
@ -204,7 +206,7 @@ if VERSION >= v"1.1"
@test m.β == [0, 0, 0, 0] # initβ(32)
@test m.γ == [1, 1, 1, 1] # initγ(32)
y = trainmode(m, x)
y = evalwgrad(m, x)
#julia> x
#[:, :, 1] =
@ -263,7 +265,7 @@ if VERSION >= v"1.1"
@test isapprox(y, out, atol = 1.0e-7)
end
let m = trainmode(GroupNorm(2,2)), sizes = (2, 4, 1, 2, 3),
let m = trainmode!(GroupNorm(2,2)), sizes = (2, 4, 1, 2, 3),
x = Float32.(reshape(collect(1:prod(sizes)), sizes))
y = reshape(permutedims(x, [3, 1, 2, 4, 5]), :, 2, 3)
y = reshape(m(y), sizes...)
@ -273,20 +275,20 @@ if VERSION >= v"1.1"
# check that μ, σ², and the output are the correct size for higher rank tensors
let m = GroupNorm(4,2), sizes = (5, 5, 3, 4, 4, 6),
x = Float32.(reshape(collect(1:prod(sizes)), sizes))
y = trainmode(m, x)
y = evalwgrad(m, x)
@test size(m.μ) == (m.G,1)
@test size(m.σ²) == (m.G,1)
@test size(y) == sizes
end
# show that group norm is the same as instance norm when the group size is the same as the number of channels
let IN = trainmode(InstanceNorm(4)), GN = trainmode(GroupNorm(4,4)), sizes = (2,2,3,4,5),
let IN = trainmode!(InstanceNorm(4)), GN = trainmode!(GroupNorm(4,4)), sizes = (2,2,3,4,5),
x = Float32.(reshape(collect(1:prod(sizes)), sizes))
@test IN(x) ≈ GN(x)
end
# show that group norm is the same as batch norm for a group of size 1 and batch of size 1
let BN = trainmode(BatchNorm(4)), GN = trainmode(GroupNorm(4,4)), sizes = (2,2,3,4,1),
let BN = trainmode!(BatchNorm(4)), GN = trainmode!(GroupNorm(4,4)), sizes = (2,2,3,4,1),
x = Float32.(reshape(collect(1:prod(sizes)), sizes))
@test BN(x) ≈ GN(x)
end

56
test/layers/stateless.jl
View File

@ -1,6 +1,6 @@
using Test
using Flux: onehotbatch, mse, crossentropy, logitcrossentropy,
σ, binarycrossentropy, logitbinarycrossentropy
σ, binarycrossentropy, logitbinarycrossentropy, flatten
const ϵ = 1e-7
@ -12,6 +12,20 @@ const ϵ = 1e-7
@testset "mse" begin
@test mse(ŷ, y) ≈ (.1^2 + .9^2)/2
end
@testset "mae" begin
@test Flux.mae(ŷ, y) ≈ 1/2
end
@testset "huber_loss" begin
@test Flux.huber_loss(ŷ, y) ≈ 0.20500000000000002
end
y = [123.0,456.0,789.0]
ŷ = [345.0,332.0,789.0]
@testset "msle" begin
@test Flux.msle(ŷ, y) ≈ 0.38813985859136585
end
# Now onehot y's
y = onehotbatch([1, 1, 0, 0], 0:1)
@ -51,31 +65,50 @@ const ϵ = 1e-7
end
y = [1 2 3]
y1 = [4.0 5.0 6.0]
ŷ = [4.0 5.0 6.0]
@testset "kldivergence" begin
@test Flux.kldivergence(y, y1) ≈ 4.761838062403337
@test Flux.kldivergence(ŷ, y) ≈ -1.7661057888493457
@test Flux.kldivergence(y, y) ≈ 0
end
y = [1 2 3 4]
y1 = [5.0 6.0 7.0 8.0]
ŷ = [5.0 6.0 7.0 8.0]
@testset "hinge" begin
@test Flux.hinge(y, y1) ≈ 0
@test Flux.hinge(ŷ, y) ≈ 0
@test Flux.hinge(y, 0.5 .* y) ≈ 0.125
end
@testset "squared_hinge" begin
@test Flux.squared_hinge(ŷ, y) ≈ 0
@test Flux.squared_hinge(y, 0.5 .* y) ≈ 0.0625
end
y = [0.1 0.2 0.3]
y1 = [0.4 0.5 0.6]
ŷ = [0.4 0.5 0.6]
@testset "poisson" begin
@test Flux.poisson(y, y1) ≈ 1.0160455586700767
@test Flux.poisson(ŷ, y) ≈ 0.6278353988097339
@test Flux.poisson(y, y) ≈ 0.5044459776946685
end
y = [1.0 0.5 0.3 2.4]
ŷ = [0 1.4 0.5 1.2]
@testset "dice_coeff_loss" begin
@test Flux.dice_coeff_loss(ŷ, y) ≈ 0.2799999999999999
@test Flux.dice_coeff_loss(y, y) ≈ 0.0
end
@testset "tversky_loss" begin
@test Flux.tversky_loss(ŷ, y) ≈ -0.06772009029345383
@test Flux.tversky_loss(ŷ, y, β = 0.8) ≈ -0.09490740740740744
@test Flux.tversky_loss(y, y) ≈ -0.5576923076923075
end
@testset "no spurious promotions" begin
for T in (Float32, Float64)
y = rand(T, 2)
ŷ = rand(T, 2)
for f in (mse, crossentropy, logitcrossentropy, Flux.kldivergence, Flux.hinge, Flux.poisson)
for f in (mse, crossentropy, logitcrossentropy, Flux.kldivergence, Flux.hinge, Flux.poisson,
Flux.mae, Flux.huber_loss, Flux.msle, Flux.squared_hinge, Flux.dice_coeff_loss, Flux.tversky_loss)
fwd, back = Flux.pullback(f, ŷ, y)
@test fwd isa T
@test eltype(back(one(T))[1]) == T
@ -83,3 +116,10 @@ const ϵ = 1e-7
end
end
end
@testset "helpers" begin
@testset "flatten" begin
x = randn(Float32, 10, 10, 3, 2)
@test size(flatten(x)) == (300, 2)
end
end

60
test/runtests.jl
View File

@ -1,32 +1,50 @@
using Flux, Test, Random, Statistics, Documenter
using Random
using Flux
using Flux.Data
using Test
using Random, Statistics, LinearAlgebra
using Documenter
using IterTools: ncycle
Random.seed!(0)
@testset "Flux" begin
@info "Testing Basics"
@testset "Utils" begin
include("utils.jl")
end
include("utils.jl")
include("onehot.jl")
include("optimise.jl")
include("data.jl")
@testset "Onehot" begin
include("onehot.jl")
end
@info "Testing Layers"
@testset "Optimise" begin
include("optimise.jl")
end
include("layers/basic.jl")
include("layers/normalisation.jl")
include("layers/stateless.jl")
include("layers/conv.jl")
@testset "Data" begin
include("data.jl")
end
if Flux.use_cuda[]
include("cuda/cuda.jl")
else
@warn "CUDA unavailable, not testing GPU support"
end
@testset "Layers" begin
include("layers/basic.jl")
include("layers/normalisation.jl")
include("layers/stateless.jl")
include("layers/conv.jl")
end
if VERSION >= v"1.2"
doctest(Flux)
end
@testset "CUDA" begin
if Flux.use_cuda[]
include("cuda/cuda.jl")
else
@warn "CUDA unavailable, not testing GPU support"
end
end
end
@testset "Docs" begin
if VERSION >= v"1.4"
DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive=true)
doctest(Flux)
end
end
end # testset Flux