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Flux.jl
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Merge branch 'master' into dg/params_docs

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Dhairya Gandhi 2020-03-02 12:45:30 +05:30 committed by GitHub
parent bb5350591f be38146ee9
commit cbb9a2a929
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36 changed files with 935 additions and 239 deletions
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24
.github/workflows/CompatHelper.yml vendored Normal file
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@ -0,0 +1,24 @@
name: CompatHelper
on:
schedule:
- cron: '00 00 * * *'
jobs:
CompatHelper:
runs-on: ${{ matrix.os }}
strategy:
matrix:
julia-version: [1.3]
julia-arch: [x64]
os: [ubuntu-latest]
steps:
- uses: julia-actions/setup-julia@latest
with:
version: ${{ matrix.julia-version }}
- name: Pkg.add("CompatHelper")
run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
- name: CompatHelper.main()
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: julia -e 'using CompatHelper; CompatHelper.main()'

11
.github/workflows/TagBot.yml vendored Normal file
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@ -0,0 +1,11 @@
name: TagBot
on:
schedule:
- cron: 0 * * * *
jobs:
TagBot:
runs-on: ubuntu-latest
steps:
- uses: JuliaRegistries/TagBot@v1
with:
token: ${{ secrets.GITHUB_TOKEN }}

120
Manifest.toml
View File

@ -8,15 +8,15 @@ version = "0.5.0"
[[AbstractTrees]] [[AbstractTrees]]
deps = ["Markdown"] deps = ["Markdown"]
git-tree-sha1 = "8201f932428d25a2e2903300764515754847d87d" git-tree-sha1 = "86d092c2599f1f7bb01668bf8eb3412f98d61e47"
uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
version = "0.3.0" version = "0.3.2"
[[Adapt]] [[Adapt]]
deps = ["LinearAlgebra"] deps = ["LinearAlgebra"]
git-tree-sha1 = "82dab828020b872fa9efd3abec1152b075bc7cbf" git-tree-sha1 = "c88cfc7f9c1f9f8633cddf0b56e86302b70f64c5"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
version = "1.0.0" version = "1.0.1"
[[Base64]] [[Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
@ -34,21 +34,21 @@ version = "0.2.0"
[[CUDAapi]] [[CUDAapi]]
deps = ["Libdl", "Logging"] deps = ["Libdl", "Logging"]
git-tree-sha1 = "56a813440ac98a1aa64672ab460a1512552211a7" git-tree-sha1 = "d7ceadd8f821177d05b897c0517e94633db535fe"
uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3" uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
version = "2.1.0" version = "3.1.0"
[[CUDAdrv]] [[CUDAdrv]]
deps = ["CEnum", "CUDAapi", "Printf"] deps = ["CEnum", "CUDAapi", "Printf"]
git-tree-sha1 = "1fce616fa0806c67c133eb1d2f68f0f1a7504665" git-tree-sha1 = "01e90fa34e25776bc7c8661183d4519149ebfe59"
uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde" uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
version = "5.0.1" version = "6.0.0"
[[CUDAnative]] [[CUDAnative]]
deps = ["Adapt", "CEnum", "CUDAapi", "CUDAdrv", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "Printf", "TimerOutputs"] deps = ["Adapt", "CEnum", "CUDAapi", "CUDAdrv", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "Printf", "TimerOutputs"]
git-tree-sha1 = "6e11d5c2c91fc623952e94c4fb73f9c4db74795a" git-tree-sha1 = "f86269ff60ebe082a2806ecbce51f3cadc68afe9"
uuid = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17" uuid = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
version = "2.7.0" version = "2.10.2"
[[CodecZlib]] [[CodecZlib]]
deps = ["BinaryProvider", "Libdl", "TranscodingStreams"] deps = ["BinaryProvider", "Libdl", "TranscodingStreams"]
@ -58,15 +58,15 @@ version = "0.6.0"
[[ColorTypes]] [[ColorTypes]]
deps = ["FixedPointNumbers", "Random"] deps = ["FixedPointNumbers", "Random"]
git-tree-sha1 = "7b62b728a5f3dd6ee3b23910303ccf27e82fad5e" git-tree-sha1 = "b9de8dc6106e09c79f3f776c27c62360d30e5eb8"
uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f" uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
version = "0.8.1" version = "0.9.1"
[[Colors]] [[Colors]]
deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Printf", "Reexport"] deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Printf", "Reexport"]
git-tree-sha1 = "c9c1845d6bf22e34738bee65c357a69f416ed5d1" git-tree-sha1 = "177d8b959d3c103a6d57574c38ee79c81059c31b"
uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
version = "0.9.6" version = "0.11.2"
[[CommonSubexpressions]] [[CommonSubexpressions]]
deps = ["Test"] deps = ["Test"]
@ -74,11 +74,17 @@ git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0"
uuid = "bbf7d656-a473-5ed7-a52c-81e309532950" uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
version = "0.2.0" version = "0.2.0"
[[CompilerSupportLibraries_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "b57c5d019367c90f234a7bc7e24ff0a84971da5d"
uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
version = "0.2.0+1"
[[CuArrays]] [[CuArrays]]
deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "Libdl", "LinearAlgebra", "MacroTools", "NNlib", "Printf", "Random", "Requires", "SparseArrays", "TimerOutputs"] deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "Libdl", "LinearAlgebra", "MacroTools", "NNlib", "Printf", "Random", "Requires", "SparseArrays", "TimerOutputs"]
git-tree-sha1 = "51fbe053dea29ed2513e02d38380007310cf4c4b" git-tree-sha1 = "7c20c5a45bb245cf248f454d26966ea70255b271"
uuid = "3a865a2d-5b23-5a0f-bc46-62713ec82fae" uuid = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
version = "1.6.0" version = "1.7.2"
[[DataAPI]] [[DataAPI]]
git-tree-sha1 = "674b67f344687a88310213ddfa8a2b3c76cc4252" git-tree-sha1 = "674b67f344687a88310213ddfa8a2b3c76cc4252"
@ -87,9 +93,9 @@ version = "1.1.0"
[[DataStructures]] [[DataStructures]]
deps = ["InteractiveUtils", "OrderedCollections"] deps = ["InteractiveUtils", "OrderedCollections"]
git-tree-sha1 = "f784254f428fb8fd7ac15982e5862a38a44523d3" git-tree-sha1 = "5a431d46abf2ef2a4d5d00bd0ae61f651cf854c8"
uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
version = "0.17.7" version = "0.17.10"
[[Dates]] [[Dates]]
deps = ["Printf"] deps = ["Printf"]
@ -107,9 +113,9 @@ version = "1.0.2"
[[DiffRules]] [[DiffRules]]
deps = ["NaNMath", "Random", "SpecialFunctions"] deps = ["NaNMath", "Random", "SpecialFunctions"]
git-tree-sha1 = "10dca52cf6d4a62d82528262921daf63b99704a2" git-tree-sha1 = "eb0c34204c8410888844ada5359ac8b96292cfd1"
uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
version = "1.0.0" version = "1.0.1"
[[Distributed]] [[Distributed]]
deps = ["Random", "Serialization", "Sockets"] deps = ["Random", "Serialization", "Sockets"]
@ -123,26 +129,26 @@ version = "1.2.0"
[[FFTW_jll]] [[FFTW_jll]]
deps = ["Libdl", "Pkg"] deps = ["Libdl", "Pkg"]
git-tree-sha1 = "05674f209a6e3387dd103a945b0113eeb64b1a58" git-tree-sha1 = "ddb57f4cf125243b4aa4908c94d73a805f3cbf2c"
uuid = "f5851436-0d7a-5f13-b9de-f02708fd171a" uuid = "f5851436-0d7a-5f13-b9de-f02708fd171a"
version = "3.3.9+3" version = "3.3.9+4"
[[FillArrays]] [[FillArrays]]
deps = ["LinearAlgebra", "Random", "SparseArrays"] deps = ["LinearAlgebra", "Random", "SparseArrays"]
git-tree-sha1 = "fec413d4fc547992eb62a5c544cedb6d7853c1f5" git-tree-sha1 = "85c6b57e2680fa28d5c8adc798967377646fbf66"
uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
version = "0.8.4" version = "0.8.5"
[[FixedPointNumbers]] [[FixedPointNumbers]]
git-tree-sha1 = "d14a6fa5890ea3a7e5dcab6811114f132fec2b4b" git-tree-sha1 = "4aaea64dd0c30ad79037084f8ca2b94348e65eaa"
uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
version = "0.6.1" version = "0.7.1"
[[ForwardDiff]] [[ForwardDiff]]
deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"] deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
git-tree-sha1 = "840700059391d36e2498d89c2e82c08f261f2a2a" git-tree-sha1 = "88b082d492be6b63f967b6c96b352e25ced1a34c"
uuid = "f6369f11-7733-5829-9624-2563aa707210" uuid = "f6369f11-7733-5829-9624-2563aa707210"
version = "0.10.8" version = "0.10.9"
[[GPUArrays]] [[GPUArrays]]
deps = ["AbstractFFTs", "Adapt", "LinearAlgebra", "Printf", "Random", "Serialization"] deps = ["AbstractFFTs", "Adapt", "LinearAlgebra", "Printf", "Random", "Serialization"]
@ -152,9 +158,9 @@ version = "2.0.1"
[[IRTools]] [[IRTools]]
deps = ["InteractiveUtils", "MacroTools", "Test"] deps = ["InteractiveUtils", "MacroTools", "Test"]
git-tree-sha1 = "72421971e60917b8cd7737f9577c4f0f87eab306" git-tree-sha1 = "1a4355e4b5b50be2311ebb644f34f3306dbd0410"
uuid = "7869d1d1-7146-5819-86e3-90919afe41df" uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
version = "0.3.0" version = "0.3.1"
[[IntelOpenMP_jll]] [[IntelOpenMP_jll]]
deps = ["Libdl", "Pkg"] deps = ["Libdl", "Pkg"]
@ -167,10 +173,10 @@ deps = ["Markdown"]
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
[[Juno]] [[Juno]]
deps = ["Base64", "Logging", "Media", "Profile", "Test"] deps = ["Base64", "Logging", "Media", "Profile"]
git-tree-sha1 = "30d94657a422d09cb97b6f86f04f750fa9c50df8" git-tree-sha1 = "4f2249fb58cfb140eeb89428e31791e2f8959d8c"
uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d" uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
version = "0.7.2" version = "0.8.0"
[[LLVM]] [[LLVM]]
deps = ["CEnum", "Libdl", "Printf", "Unicode"] deps = ["CEnum", "Libdl", "Printf", "Unicode"]
@ -192,16 +198,16 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
[[MKL_jll]] [[MKL_jll]]
deps = ["Libdl", "Pkg"] deps = ["IntelOpenMP_jll", "Libdl", "Pkg"]
git-tree-sha1 = "61069ae718b8ab1e325bbfb4e5268902e7ea08e3" git-tree-sha1 = "720629cc8cbd12c146ca01b661fd1a6cf66e2ff4"
uuid = "856f044c-d86e-5d09-b602-aeab76dc8ba7" uuid = "856f044c-d86e-5d09-b602-aeab76dc8ba7"
version = "2019.0.117+0" version = "2019.0.117+2"
[[MacroTools]] [[MacroTools]]
deps = ["DataStructures", "Markdown", "Random"] deps = ["DataStructures", "Markdown", "Random"]
git-tree-sha1 = "e2fc7a55bb2224e203bbd8b59f72b91323233458" git-tree-sha1 = "07ee65e03e28ca88bc9a338a3726ae0c3efaa94b"
uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
version = "0.5.3" version = "0.5.4"
[[Markdown]] [[Markdown]]
deps = ["Base64"] deps = ["Base64"]
@ -224,9 +230,9 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
[[NNlib]] [[NNlib]]
deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics"] deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics"]
git-tree-sha1 = "135c0de4794d5e214b06f1fb4787af4a72896e61" git-tree-sha1 = "755c0bab3912ff782167e1b4b774b833f8a0e550"
uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
version = "0.6.2" version = "0.6.4"
[[NaNMath]] [[NaNMath]]
git-tree-sha1 = "928b8ca9b2791081dc71a51c55347c27c618760f" git-tree-sha1 = "928b8ca9b2791081dc71a51c55347c27c618760f"
@ -234,10 +240,10 @@ uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
version = "0.3.3" version = "0.3.3"
[[OpenSpecFun_jll]] [[OpenSpecFun_jll]]
deps = ["Libdl", "Pkg"] deps = ["CompilerSupportLibraries_jll", "Libdl", "Pkg"]
git-tree-sha1 = "65f672edebf3f4e613ddf37db9dcbd7a407e5e90" git-tree-sha1 = "d110040968b9afe95c6bd9c6233570b0fe8abd22"
uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e" uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
version = "0.5.3+1" version = "0.5.3+2"
[[OrderedCollections]] [[OrderedCollections]]
deps = ["Random", "Serialization", "Test"] deps = ["Random", "Serialization", "Test"]
@ -246,7 +252,7 @@ uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
version = "1.1.0" version = "1.1.0"
[[Pkg]] [[Pkg]]
deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"] deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Test", "UUIDs"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
[[Printf]] [[Printf]]
@ -273,9 +279,9 @@ version = "0.2.0"
[[Requires]] [[Requires]]
deps = ["UUIDs"] deps = ["UUIDs"]
git-tree-sha1 = "999513b7dea8ac17359ed50ae8ea089e4464e35e" git-tree-sha1 = "d37400976e98018ee840e0ca4f9d20baa231dc6b"
uuid = "ae029012-a4dd-5104-9daa-d747884805df" uuid = "ae029012-a4dd-5104-9daa-d747884805df"
version = "1.0.0" version = "1.0.1"
[[SHA]] [[SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
@ -298,9 +304,9 @@ uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[[SpecialFunctions]] [[SpecialFunctions]]
deps = ["OpenSpecFun_jll"] deps = ["OpenSpecFun_jll"]
git-tree-sha1 = "268052ee908b2c086cc0011f528694f02f3e2408" git-tree-sha1 = "e19b98acb182567bcb7b75bb5d9eedf3a3b5ec6c"
uuid = "276daf66-3868-5448-9aa4-cd146d93841b" uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
version = "0.9.0" version = "0.10.0"
[[StaticArrays]] [[StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"] deps = ["LinearAlgebra", "Random", "Statistics"]
@ -314,9 +320,9 @@ uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
[[StatsBase]] [[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"] deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
git-tree-sha1 = "c53e809e63fe5cf5de13632090bc3520649c9950" git-tree-sha1 = "be5c7d45daa449d12868f4466dbf5882242cf2d9"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.32.0" version = "0.32.1"
[[Test]] [[Test]]
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"] deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
@ -343,21 +349,23 @@ uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[ZipFile]] [[ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"] deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "5de8320a46812da1a8ca98b16a8a4546d44efa62" git-tree-sha1 = "8748302cfdec02c4ae9c97b112cf10003f7f767f"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.9.0" version = "0.9.1"
[[Zlib_jll]] [[Zlib_jll]]
deps = ["Libdl", "Pkg"] deps = ["Libdl", "Pkg"]
git-tree-sha1 = "5618a43055eb09377edca21d19d0e99bce24a9c3" git-tree-sha1 = "fd36a6739e256527287c5444960d0266712cd49e"
uuid = "83775a58-1f1d-513f-b197-d71354ab007a" uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.11+7" version = "1.2.11+8"
[[Zygote]] [[Zygote]]
deps = ["DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"] deps = ["DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "74382bcc4c1e8075e14554da67d75565f8fb7827" git-tree-sha1 = "3c65158c0aa0808cdfff8bca2a36430b038aad00"
repo-rev = "master"
repo-url = "https://github.com/FluxML/Zygote.jl.git"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.4.5" version = "0.4.7"
[[ZygoteRules]] [[ZygoteRules]]
deps = ["MacroTools"] deps = ["MacroTools"]

11
Project.toml
View File

@ -1,6 +1,6 @@
name = "Flux" name = "Flux"
uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c" uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
version = "0.10.1" version = "0.10.2"
[deps] [deps]
AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@ -27,9 +27,9 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
AbstractTrees = "0.2, 0.3" AbstractTrees = "0.2, 0.3"
Adapt = "1" Adapt = "1"
CodecZlib = "0.5, 0.6" CodecZlib = "0.5, 0.6"
Colors = "0.8, 0.9" Colors = "0.8, 0.9, 0.10, 0.11"
CuArrays = "1.6" CuArrays = "1.6"
Juno = "0.5, 0.6, 0.7" Juno = "0.5, 0.6, 0.7, 0.8"
MacroTools = "0.3, 0.4, 0.5" MacroTools = "0.3, 0.4, 0.5"
NNlib = "0.6" NNlib = "0.6"
Reexport = "0.2" Reexport = "0.2"
@ -40,7 +40,10 @@ julia = "1"
[extras] [extras]
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[targets] [targets]
test = ["Test", "Documenter"] test = ["Test", "Documenter", "IterTools", "LinearAlgebra"]

8
docs/make.jl
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@ -14,13 +14,17 @@ makedocs(modules=[Flux, NNlib],
"Recurrence" => "models/recurrence.md", "Recurrence" => "models/recurrence.md",
"Regularisation" => "models/regularisation.md", "Regularisation" => "models/regularisation.md",
"Model Reference" => "models/layers.md", "Model Reference" => "models/layers.md",
"Advanced Model Building" => "models/advanced.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" => "Training Models" =>
["Optimisers" => "training/optimisers.md", ["Optimisers" => "training/optimisers.md",
"Training" => "training/training.md"], "Training" => "training/training.md"],
"One-Hot Encoding" => "data/onehot.md",
"GPU Support" => "gpu.md", "GPU Support" => "gpu.md",
"Saving & Loading" => "saving.md", "Saving & Loading" => "saving.md",
"The Julia Ecosystem" => "ecosystem.md",
"Performance Tips" => "performance.md", "Performance Tips" => "performance.md",
"Community" => "community.md"], "Community" => "community.md"],
format = Documenter.HTML(assets = ["assets/flux.css"], format = Documenter.HTML(assets = ["assets/flux.css"],

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
```

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

25
docs/src/models/basics.md
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@ -69,8 +69,8 @@ b = rand(2)
predict(x) = W*x .+ b predict(x) = W*x .+ b
function loss(x, y) function loss(x, y)
ŷ = predict(x) ŷ = predict(x)
sum((y .- ŷ).^2) sum((y .- ŷ).^2)
end end
x, y = rand(5), rand(2) # Dummy data x, y = rand(5), rand(2) # Dummy data
@ -221,3 +221,24 @@ 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). 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](advacned.md). For some more helpful tricks, including parameter freezing, please checkout the [advanced usage guide](advacned.md).
## Utility functions
Flux provides some utility functions to help you generate models in an automated fashion.
`outdims` enables you to calculate the spatial output dimensions of layers like `Conv` when applied to input images of a given size.
Currently limited to the following layers:
- `Chain`
- `Dense`
- `Conv`
- `Diagonal`
- `Maxout`
- `ConvTranspose`
- `DepthwiseConv`
- `CrossCor`
- `MaxPool`
- `MeanPool`
```@docs
outdims
```

39
docs/src/models/layers.md
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@ -40,19 +40,6 @@ Maxout
SkipConnection 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 ## Normalisation & Regularisation
@ -61,19 +48,29 @@ These layers don't affect the structure of the network but may improve training
```@docs ```@docs
BatchNorm BatchNorm
Dropout Dropout
Flux.dropout
AlphaDropout AlphaDropout
LayerNorm LayerNorm
GroupNorm 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 `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
testmode!
trainmode!
```
## Cost Functions ## Cost Functions
```@docs ```@docs
mse Flux.mse
crossentropy Flux.crossentropy
logitcrossentropy Flux.logitcrossentropy
binarycrossentropy Flux.binarycrossentropy
logitbinarycrossentropy Flux.logitbinarycrossentropy
kldivergence Flux.kldivergence
poisson Flux.poisson
hinge Flux.hinge
``` ```

37
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@ -0,0 +1,37 @@
# 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.elu
NNlib.gelu
NNlib.leakyrelu
NNlib.logcosh
NNlib.logsigmoid
NNlib.sigmoid
NNlib.relu
NNlib.selu
NNlib.softplus
NNlib.softsign
NNlib.swish
```
## Softmax
```@docs
NNlib.softmax
NNlib.logsoftmax
```
## Pooling
```@docs
NNlib.maxpool
NNlib.meanpool
```
## Convolution
```@docs
NNlib.conv
NNlib.depthwiseconv
```

4
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@ -31,7 +31,7 @@ julia> params(m)
param([0.0, 0.0, 0.0, 0.0, 0.0]) param([0.0, 0.0, 0.0, 0.0, 0.0])
julia> sum(norm, params(m)) julia> sum(norm, params(m))
26.01749952921026 (tracked) 26.01749952921026
``` ```
Here's a larger example with a multi-layer perceptron. 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
julia> using Flux: activations 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) Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
julia> activations(c, rand(10)) julia> activations(c, rand(10))

11
docs/src/training/optimisers.md
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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: We want to update each parameter, using the gradient, in order to improve (reduce) the loss. Here's one way to do that:
```julia ```julia
using Flux: update! using Flux.Optimise: update!
η = 0.1 # Learning Rate η = 0.1 # Learning Rate
for p in (W, b) for p in (W, b)
@ -46,6 +46,7 @@ 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. All optimisers return an object that, when passed to `train!`, will update the parameters passed to it.
```@docs ```@docs
Flux.Optimise.update!
Descent Descent
Momentum Momentum
Nesterov Nesterov
@ -61,7 +62,7 @@ ADAMW
## Optimiser Interface ## 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. 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 +100,15 @@ Flux internally calls on this function via the `update!` function. It shares the
## Composing Optimisers ## 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. 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 ```julia
opt = Optimiser(ExpDecay(0.001, 0.1, 1000, 1e-4), Descent()) 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. 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 optimser. It is then applied like any optimiser.
```julia ```julia
w = randn(10, 10) w = randn(10, 10)

19
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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. * A collection of data points that will be provided to the objective function.
* An [optimiser](optimisers.md) that will update the model parameters appropriately. * 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 ```@docs
Flux.train!(objective, params, data, opt) Flux.Optimise.train!
``` ```
There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo). There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo).
@ -58,7 +58,8 @@ data = [(x, y)]
```julia ```julia
data = [(x, y), (x, y), (x, y)] data = [(x, y), (x, y), (x, y)]
# Or equivalently # 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`: It's common to load the `x`s and `y`s separately. In this case you can use `zip`:
@ -69,6 +70,14 @@ ys = [rand( 10), rand( 10), rand( 10)]
data = zip(xs, ys) 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"). 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`. A convenient way to run multiple epochs from the REPL is provided by `@epochs`.
@ -122,7 +131,7 @@ 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. 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. E.g. in the places marked with comments.
``` ```julia
function my_custom_train!(loss, ps, data, opt) function my_custom_train!(loss, ps, data, opt)
ps = Params(ps) ps = Params(ps)
for d in data for d in data

3
src/Flux.jl
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@ -7,11 +7,12 @@ using Zygote, MacroTools, Juno, Reexport, Statistics, Random
using MacroTools: @forward using MacroTools: @forward
@reexport using NNlib @reexport using NNlib
using Zygote: Params, @adjoint, gradient, pullback, @nograd using Zygote: Params, @adjoint, gradient, pullback, @nograd
export gradient export gradient
export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool, export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,
DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm, 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") include("optimise/Optimise.jl")
using .Optimise using .Optimise

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

88
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@ -0,0 +1,88 @@
# 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.
Example usage:
Xtrain = rand(10, 100)
dtrain = DataLoader(Xtrain, batchsize=2)
# iterate over 50 mini-batches
for x in dtrain:
@assert size(x) == (10, 2)
...
end
Xtrain = rand(10, 100)
Ytrain = rand(100)
dtrain = DataLoader(Xtrain, Ytrain, batchsize=2, shuffle=true)
for epoch in 1:100
for (x, y) in dtrain:
@assert size(x) == (10, 2)
@assert size(y) == (2,)
...
end
end
# train for 10 epochs
using IterTools: ncycle
Flux.train!(loss, ps, ncycle(dtrain, 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

136
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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)https://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

2
src/data/iris.jl
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@ -28,7 +28,6 @@ function load()
end end
""" """
labels() labels()
Get the labels of the iris dataset, a 150 element array of strings listing the Get the labels of the iris dataset, a 150 element array of strings listing the
@ -53,7 +52,6 @@ function labels()
end end
""" """
features() features()
Get the features of the iris dataset. This is a 4x150 matrix of Float64 Get the features of the iris dataset. This is a 4x150 matrix of Float64

32
src/functor.jl
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@ -39,6 +39,38 @@ end
trainable(m) = functor(m)[1] 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) params!(p::Params, x::AbstractArray{<:Number}, seen = IdSet()) = push!(p, x)
function params!(p::Params, x, seen = IdSet()) function params!(p::Params, x, seen = IdSet())

29
src/layers/basic.jl
View File

@ -33,12 +33,25 @@ applychain(fs::Tuple, x) = applychain(tail(fs), first(fs)(x))
Base.getindex(c::Chain, i::AbstractArray) = Chain(c.layers[i]...) 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) function Base.show(io::IO, c::Chain)
print(io, "Chain(") print(io, "Chain(")
join(io, c.layers, ", ") join(io, c.layers, ", ")
print(io, ")") print(io, ")")
end end
"""
outdims(c::Chain, isize)
Calculate the output dimensions given the input dimensions, `isize`.
```julia
m = Chain(Conv((3, 3), 3 => 16), Conv((3, 3), 16 => 32))
outdims(m, (10, 10)) == (6, 6)
```
"""
outdims(c::Chain, isize) = foldl(∘, map(l -> (x -> outdims(l, x)), c.layers))(isize)
# This is a temporary and naive implementation # This is a temporary and naive implementation
# it might be replaced in the future for better performance # it might be replaced in the future for better performance
@ -116,6 +129,19 @@ end
(a::Dense{<:Any,W})(x::AbstractArray{<:AbstractFloat}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = (a::Dense{<:Any,W})(x::AbstractArray{<:AbstractFloat}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
a(T.(x)) a(T.(x))
"""
outdims(l::Dense, isize)
Calculate the output dimensions given the input dimensions, `isize`.
```julia
m = Dense(10, 5)
outdims(m, (5, 2)) == (5,)
outdims(m, (10,)) == (5,)
```
"""
outdims(l::Dense, isize) = (size(l.W)[1],)
""" """
Diagonal(in::Integer) Diagonal(in::Integer)
@ -145,6 +171,7 @@ function Base.show(io::IO, l::Diagonal)
print(io, "Diagonal(", length(l.α), ")") print(io, "Diagonal(", length(l.α), ")")
end end
outdims(l::Diagonal, isize) = (length(l.α),)
""" """
Maxout(over) Maxout(over)
@ -193,6 +220,8 @@ function (mo::Maxout)(input::AbstractArray)
mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over) mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over)
end end
outdims(l::Maxout, isize) = outdims(first(l.over), isize)
""" """
SkipConnection(layers, connection) SkipConnection(layers, connection)

37
src/layers/conv.jl
View File

@ -1,4 +1,9 @@
using NNlib: conv, ∇conv_data, depthwiseconv using NNlib: conv, ∇conv_data, depthwiseconv, output_size
# pad dims of x with dims of y until ndims(x) == ndims(y)
_paddims(x::Tuple, y::Tuple) = (x..., y[(end - (length(y) - length(x) - 1)):end]...)
_convtransoutdims(isize, ksize, ssize, dsize, pad) = (isize .- 1).*ssize .+ 1 .+ (ksize .- 1).*dsize .- (pad[1:2:end] .+ pad[2:2:end])
expand(N, i::Tuple) = i expand(N, i::Tuple) = i
expand(N, i::Integer) = ntuple(_ -> i, N) expand(N, i::Integer) = ntuple(_ -> i, N)
@ -17,7 +22,7 @@ Example: Applying Conv layer to a 1-channel input using a 2x2 window size,
out = 16 out = 16
Conv((2, 2), 1=>16, relu) Conv((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, 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. and a batch of 50 would be a `100×100×3×50` array.
@ -68,6 +73,21 @@ end
(a::Conv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = (a::Conv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
a(T.(x)) a(T.(x))
"""
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`.
```julia
m = Conv((3, 3), 3 => 16)
outdims(m, (10, 10)) == (8, 8)
outdims(m, (10, 10, 1, 3)) == (8, 8)
```
"""
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)
ConvTranspose(size, in=>out, relu) ConvTranspose(size, in=>out, relu)
@ -140,6 +160,9 @@ end
(a::ConvTranspose{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = (a::ConvTranspose{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
a(T.(x)) a(T.(x))
outdims(l::ConvTranspose{N}, isize) where N = _convtransoutdims(isize[1:2], size(l.weight)[1:N], l.stride, l.dilation, l.pad)
""" """
DepthwiseConv(size, in=>out) DepthwiseConv(size, in=>out)
DepthwiseConv(size, in=>out, relu) DepthwiseConv(size, in=>out, relu)
@ -204,6 +227,9 @@ end
(a::DepthwiseConv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = (a::DepthwiseConv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
a(T.(x)) a(T.(x))
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)
CrossCor(size, in=>out, relu) CrossCor(size, in=>out, relu)
@ -275,6 +301,9 @@ end
(a::CrossCor{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = (a::CrossCor{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
a(T.(x)) a(T.(x))
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) MaxPool(k)
@ -304,6 +333,8 @@ function Base.show(io::IO, m::MaxPool)
print(io, "MaxPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")") print(io, "MaxPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
end 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)
@ -331,3 +362,5 @@ end
function Base.show(io::IO, m::MeanPool) function Base.show(io::IO, m::MeanPool)
print(io, "MeanPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")") print(io, "MeanPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
end 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))

73
src/layers/normalise.jl
View File

@ -2,11 +2,23 @@ istraining() = false
@adjoint istraining() = true, _ -> nothing @adjoint istraining() = true, _ -> nothing
_isactive(m) = isnothing(m.active) ? istraining() : m.active
_dropout_shape(s, ::Colon) = size(s) _dropout_shape(s, ::Colon) = size(s)
_dropout_shape(s, dims) = tuple((i ∉ dims ? 1 : si for (i, si) ∈ enumerate(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_kernel(y::T, p, q) where {T} = y > p ? T(1 / q) : T(0)
"""
dropout(p, dims = :)
Dropout function. For each input, either sets that input to `0` (with probability
`p`) or scales it by `1/(1-p)`. The `dims` argument is to specify 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(x, p; dims = :) = x dropout(x, p; dims = :) = x
@adjoint function dropout(x, p; dims = :) @adjoint function dropout(x, p; dims = :)
@ -18,22 +30,28 @@ end
""" """
Dropout(p, dims = :) Dropout(p, dims = :)
A Dropout layer. For each input, either sets that input to `0` (with probability A Dropout layer. In the forward pass, applies the [`dropout`](@ref) function on the input.
`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 Does nothing to the input once [`testmode!`](@ref) is false.
used as a regularisation, i.e. it reduces overfitting during training. see also [`dropout`](@ref).
""" """
mutable struct Dropout{F,D} mutable struct Dropout{F,D}
p::F p::F
dims::D dims::D
active::Union{Bool, Nothing}
end end
function Dropout(p; dims = :) function Dropout(p; dims = :)
@assert 0 ≤ p ≤ 1 @assert 0 ≤ p ≤ 1
Dropout{typeof(p),typeof(dims)}(p, dims) Dropout{typeof(p),typeof(dims)}(p, dims, nothing)
end 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) function Base.show(io::IO, d::Dropout)
print(io, "Dropout(", d.p) print(io, "Dropout(", d.p)
@ -43,20 +61,24 @@ end
""" """
AlphaDropout(p) AlphaDropout(p)
A dropout layer. It is used in Self-Normalizing Neural Networks. A dropout layer. It is used in Self-Normalizing Neural Networks.
(https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf) (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. The AlphaDropout layer ensures that mean and variance of activations remains the same as before.
Does nothing to the input once [`testmode!`](@ref) is false.
""" """
mutable struct AlphaDropout{F} mutable struct AlphaDropout{F}
p::F p::F
function AlphaDropout(p) active::Union{Bool, Nothing}
function AlphaDropout(p, active = nothing)
@assert 0 ≤ p ≤ 1 @assert 0 ≤ p ≤ 1
new{typeof(p)}(p) new{typeof(p)}(p, active)
end end
end end
function (a::AlphaDropout)(x) function (a::AlphaDropout)(x)
istraining() || return x _isactive(a) || return x
λ = eltype(x)(1.0507009873554804934193349852946) λ = eltype(x)(1.0507009873554804934193349852946)
α = eltype(x)(1.6732632423543772848170429916717) α = eltype(x)(1.6732632423543772848170429916717)
α1 = eltype(x)(-λ*α) α1 = eltype(x)(-λ*α)
@ -68,6 +90,9 @@ function (a::AlphaDropout)(x)
return x return x
end end
testmode!(m::AlphaDropout, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
""" """
LayerNorm(h::Integer) LayerNorm(h::Integer)
@ -106,6 +131,8 @@ it's the usual channel dimension.)
shifts them to have a new mean and variance (corresponding to the learnable, shifts them to have a new mean and variance (corresponding to the learnable,
per-channel `bias` and `scale` parameters). per-channel `bias` and `scale` parameters).
Use [`testmode!`](@ref) during inference.
See [Batch Normalization: Accelerating Deep Network Training by Reducing See [Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf). Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf).
@ -127,12 +154,13 @@ mutable struct BatchNorm{F,V,W,N}
σ²::W # moving std σ²::W # moving std
ϵ::N ϵ::N
momentum::N momentum::N
active::Union{Bool, Nothing}
end end
BatchNorm(chs::Integer, λ = identity; BatchNorm(chs::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) = initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
BatchNorm(λ, initβ(chs), initγ(chs), 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) trainable(bn::BatchNorm) = (bn.β, bn.γ)
@ -145,7 +173,7 @@ function (BN::BatchNorm)(x)
m = div(prod(size(x)), channels) m = div(prod(size(x)), channels)
γ = reshape(BN.γ, affine_shape...) γ = reshape(BN.γ, affine_shape...)
β = reshape(BN.β, affine_shape...) β = reshape(BN.β, affine_shape...)
if !istraining() if !_isactive(BN)
μ = reshape(BN.μ, affine_shape...) μ = reshape(BN.μ, affine_shape...)
σ² = reshape(BN.σ², affine_shape...) σ² = reshape(BN.σ², affine_shape...)
ϵ = BN.ϵ ϵ = BN.ϵ
@ -170,6 +198,9 @@ end
@functor BatchNorm @functor BatchNorm
testmode!(m::BatchNorm, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
function Base.show(io::IO, l::BatchNorm) function Base.show(io::IO, l::BatchNorm)
print(io, "BatchNorm($(join(size(l.β), ", "))") print(io, "BatchNorm($(join(size(l.β), ", "))")
(l.λ == identity) || print(io, ", λ = $(l.λ)") (l.λ == identity) || print(io, ", λ = $(l.λ)")
@ -193,6 +224,8 @@ it's the usual channel dimension.)
shifts them to have a new mean and variance (corresponding to the learnable, shifts them to have a new mean and variance (corresponding to the learnable,
per-channel `bias` and `scale` parameters). per-channel `bias` and `scale` parameters).
Use [`testmode!`](@ref) during inference.
See [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022). See [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022).
Example: Example:
@ -215,12 +248,13 @@ mutable struct InstanceNorm{F,V,W,N}
σ²::W # moving std σ²::W # moving std
ϵ::N ϵ::N
momentum::N momentum::N
active::Union{Bool, Nothing}
end end
InstanceNorm(chs::Integer, λ = identity; InstanceNorm(chs::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) = initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
InstanceNorm(λ, initβ(chs), initγ(chs), 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) trainable(in::InstanceNorm) = (in.β, in.γ)
@ -237,7 +271,7 @@ function (in::InstanceNorm)(x)
m = div(prod(size(x)), c*bs) m = div(prod(size(x)), c*bs)
γ, β = expand_inst(in.γ, affine_shape), expand_inst(in.β, affine_shape) 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) μ = expand_inst(in.μ, affine_shape)
σ² = expand_inst(in.σ², affine_shape) σ² = expand_inst(in.σ², affine_shape)
ϵ = in.ϵ ϵ = in.ϵ
@ -263,6 +297,9 @@ end
@functor InstanceNorm @functor InstanceNorm
testmode!(m::InstanceNorm, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
function Base.show(io::IO, l::InstanceNorm) function Base.show(io::IO, l::InstanceNorm)
print(io, "InstanceNorm($(join(size(l.β), ", "))") print(io, "InstanceNorm($(join(size(l.β), ", "))")
(l.λ == identity) || print(io, ", λ = $(l.λ)") (l.λ == identity) || print(io, ", λ = $(l.λ)")
@ -283,6 +320,8 @@ 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 would be computed.
The number of channels must be an integer multiple of the number of groups. The number of channels must be an integer multiple of the number of groups.
Use [`testmode!`](@ref) during inference.
Example: Example:
``` ```
m = Chain(Conv((3,3), 1=>32, leakyrelu;pad = 1), m = Chain(Conv((3,3), 1=>32, leakyrelu;pad = 1),
@ -300,12 +339,13 @@ mutable struct GroupNorm{F,V,W,N,T}
σ²::W # moving std σ²::W # moving std
ϵ::N ϵ::N
momentum::N momentum::N
active::Union{Bool, Nothing}
end end
GroupNorm(chs::Integer, G::Integer, λ = identity; GroupNorm(chs::Integer, G::Integer, λ = identity;
initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) = initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
GroupNorm(G, λ, initβ(chs), initγ(chs), 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) trainable(gn::GroupNorm) = (gn.β, gn.γ)
@ -329,7 +369,7 @@ function(gn::GroupNorm)(x)
β = reshape(gn.β, affine_shape...) β = reshape(gn.β, affine_shape...)
y = reshape(x,((size(x))[1:end-2]...,channels_per_group,groups,batches)) y = reshape(x,((size(x))[1:end-2]...,channels_per_group,groups,batches))
if !istraining() if !_isactive(gn)
og_shape = size(x) og_shape = size(x)
μ = reshape(gn.μ, μ_affine_shape...) # Shape : (1,1,...C/G,G,1) 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) σ² = reshape(gn.σ², μ_affine_shape...) # Shape : (1,1,...C/G,G,1)
@ -360,6 +400,9 @@ end
@functor GroupNorm @functor GroupNorm
testmode!(m::GroupNorm, mode = true) =
(m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
function Base.show(io::IO, l::GroupNorm) function Base.show(io::IO, l::GroupNorm)
print(io, "GroupNorm($(join(size(l.β), ", "))") print(io, "GroupNorm($(join(size(l.β), ", "))")
(l.λ == identity) || print(io, ", λ = $(l.λ)") (l.λ == identity) || print(io, ", λ = $(l.λ)")

3
src/layers/recurrent.jl
View File

@ -45,8 +45,7 @@ Base.show(io::IO, m::Recur) = print(io, "Recur(", m.cell, ")")
""" """
reset!(rnn) reset!(rnn)
Reset the hidden state of a recurrent layer back to its original value. See also Reset the hidden state of a recurrent layer back to its original value.
`truncate!`.
Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to

87
src/layers/stateless.jl
View File

@ -1,10 +1,12 @@
using CuArrays
using NNlib: logsoftmax, logσ
# Cost functions # Cost functions
"""
mse(ŷ, y)
Return the mean squared error `sum((ŷ .- y).^2) / length(y)`.
"""
mse(ŷ, y) = sum((ŷ .- y).^2) * 1 // length(y) mse(ŷ, y) = sum((ŷ .- y).^2) * 1 // length(y)
function _crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, weight::Nothing) function _crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, weight::Nothing)
return -sum(y .* log.(ŷ)) * 1 // size(y, 2) return -sum(y .* log.(ŷ)) * 1 // size(y, 2)
end end
@ -17,10 +19,26 @@ function _crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, weight::Abstr
return -sum(y .* log.(ŷ) .* weight) * 1 // size(y, 2) return -sum(y .* log.(ŷ) .* weight) * 1 // size(y, 2)
end end
"""
crossentropy(ŷ, y; weight=1)
Return the crossentropy computed as `-sum(y .* log.(ŷ) .* weight) / size(y, 2)`.
See also [`logitcrossentropy`](@ref), [`binarycrossentropy`](@ref).
"""
crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight=nothing) = _crossentropy(ŷ, y, weight) 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 [softmax](@ref) operation:
-sum(y .* logsoftmax(ŷ) .* weight) / size(y, 2)
See also [`crossentropy`](@ref), [`binarycrossentropy`](@ref).
"""
function logitcrossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight = 1)
return -sum(y .* logsoftmax(ŷ) .* weight) * 1 // size(y, 2)
end end
""" """
@ -28,11 +46,7 @@ end
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.]) Typically, the prediction `ŷ` is given by the output of a [`sigmoid`](@ref) activation.
3-element Array{Float64,1}:
1.4244
0.352317
0.86167
""" """
binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -y*log(ŷ + ϵ) - (1 - y)*log(1 - ŷ + ϵ) binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -y*log(ŷ + ϵ) - (1 - y)*log(1 - ŷ + ϵ)
@ -40,44 +54,42 @@ 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) 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)` `logitbinarycrossentropy(ŷ, y)` is mathematically equivalent to `binarycrossentropy(σ(ŷ), y)`
but it is more numerically stable. but it is more numerically stable.
julia> logitbinarycrossentropy.([-1.1491, 0.8619, 0.3127], [1, 1, 0.]) See also [`binarycrossentropy`](@ref), [`sigmoid`](@ref), [`logsigmoid`](@ref).
3-element Array{Float64,1}:
1.4244
0.352317
0.86167
""" """
logitbinarycrossentropy(logŷ, y) = (1 - y)*logŷ - logσ(logŷ) logitbinarycrossentropy(ŷ, y) = (1 - y)*ŷ - logσ(ŷ)
# Re-definition to fix interaction with CuArrays. # 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. Normalises `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) ```julia-repl
3×3 Array{Int64,2}: julia> a = reshape(collect(1:9), 3, 3)
1 4 7 3×3 Array{Int64,2}:
2 5 8 1 4 7
3 6 9 2 5 8
3 6 9
julia> normalise(a) julia> normalise(a)
3×3 Array{Float64,2}: 3×3 Array{Float64,2}:
-1.22474 -1.22474 -1.22474 -1.22474 -1.22474 -1.22474
0.0 0.0 0.0 0.0 0.0 0.0
1.22474 1.22474 1.22474 1.22474 1.22474 1.22474
julia> normalise(a, dims=2) julia> normalise(a, dims=2)
3×3 Array{Float64,2}: 3×3 Array{Float64,2}:
-1.22474 0.0 1.22474 -1.22474 0.0 1.22474
-1.22474 0.0 1.22474 -1.22474 0.0 1.22474
-1.22474 0.0 1.22474 -1.22474 0.0 1.22474
```
""" """
function normalise(x::AbstractArray; dims=1) function normalise(x::AbstractArray; dims=1)
μ′ = mean(x, dims = dims) μ′ = mean(x, dims = dims)
@ -87,6 +99,7 @@ end
""" """
kldivergence(ŷ, y) kldivergence(ŷ, y)
KLDivergence is a measure of how much one probability distribution is different from the other. 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. 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). [KL Divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence).
@ -99,6 +112,7 @@ end
""" """
poisson(ŷ, y) poisson(ŷ, y)
Poisson loss function is a measure of how the predicted distribution diverges from the expected distribution. 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). [Poisson Loss](https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/poisson).
""" """
@ -106,7 +120,8 @@ poisson(ŷ, y) = sum(ŷ .- y .* log.(ŷ)) *1 // size(y,2)
""" """
hinge(ŷ, y) hinge(ŷ, y)
Measures the loss given the prediction ŷ and true labels y(containing 1 or -1).
Measures the loss given the prediction `ŷ` and true labels `y` (containing 1 or -1).
[Hinge Loss](https://en.wikipedia.org/wiki/Hinge_loss). [Hinge Loss](https://en.wikipedia.org/wiki/Hinge_loss).
""" """
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)

4
src/onehot.jl
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@ -125,6 +125,4 @@ onecold(y::AbstractMatrix, labels...) =
onecold(y::OneHotMatrix, labels...) = onecold(y::OneHotMatrix, labels...) =
mapreduce(x -> Flux.onecold(x, labels...), |, y.data, dims = 2, init = 0) mapreduce(x -> Flux.onecold(x, labels...), |, y.data, dims = 2, init = 0)
# TODO probably still want this as a custom adjoint Zygote @nograd onecold, onehot, onehotbatch
# onecold(x::TrackedVector, l...) = onecold(data(x), l...)
# onecold(x::TrackedMatrix, l...) = onecold(data(x), l...)

2
src/optimise/Optimise.jl
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@ -1,6 +1,6 @@
module Optimise module Optimise
export train!, export train!, update!,
SGD, Descent, ADAM, Momentum, Nesterov, RMSProp, SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM, ADAMW,RADAM, ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM, ADAMW,RADAM,
InvDecay, ExpDecay, WeightDecay, stop, Optimiser InvDecay, ExpDecay, WeightDecay, stop, Optimiser

21
src/optimise/optimisers.jl
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@ -1,5 +1,4 @@
using Flux using Flux
using Base: @get!
using MacroTools: @forward using MacroTools: @forward
const ϵ = 1e-8 const ϵ = 1e-8
@ -7,7 +6,7 @@ const ϵ = 1e-8
# TODO: should use weak refs # TODO: should use weak refs
""" """
Descent(η) Descent(η)
Classic gradient descent optimiser with learning rate `η`. Classic gradient descent optimiser with learning rate `η`.
For each parameter `p` and its gradient `δp`, this runs `p -= η*δp` For each parameter `p` and its gradient `δp`, this runs `p -= η*δp`
@ -78,7 +77,7 @@ Gradient descent with learning rate `η` and Nesterov momentum `ρ`.
## Parameters ## Parameters
- Learning Rate (η): Amount by which the gradients are dicsounted berfore updating the weights. Defaults to `0.001`. - 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`. - Nesterov Momentum (ρ): Parameters controlling the amount of nesterov momentum to be applied. Defaults to `0.9`.
## Examples ## Examples
```julia ```julia
@ -106,7 +105,7 @@ end
""" """
RMSProp(η, ρ) RMSProp(η, ρ)
Implements the RMSProp algortihm. Often a good choice for recurrent networks. Paramters other than learning rate generally don't need tuning. Implements the RMSProp algortihm. Often a good choice for recurrent networks. Parameters other than learning rate generally don't need tuning.
## Parameters ## Parameters
- Learning Rate (η): Defaults to `0.001`. - Learning Rate (η): Defaults to `0.001`.
@ -442,17 +441,16 @@ function apply!(o::Optimiser, x, Δ)
end end
""" """
InvDecay(γ) InvDecay(γ)
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. 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.
```
## Parameters ## Parameters
- gamma (γ): Defaults to `0.001` - gamma (γ): Defaults to `0.001`
## Example ## Example
```julia ```julia
Optimiser(InvDecay(..), Opt(..)) Optimiser(InvDecay(..), Opt(..))
``` ```
""" """
mutable struct InvDecay mutable struct InvDecay
@ -471,7 +469,7 @@ function apply!(o::InvDecay, x, Δ)
end end
""" """
ExpDecay(eta, decay, decay_step, clip) ExpDecay(eta, decay, decay_step, clip)
Discount the learning rate `eta` by a multiplicative factor `decay` every `decay_step` till a minimum of `clip`. Discount the learning rate `eta` by a multiplicative factor `decay` every `decay_step` till a minimum of `clip`.
@ -484,9 +482,8 @@ Discount the learning rate `eta` by a multiplicative factor `decay` every `decay
## Example ## Example
To apply exponential decay to an optimiser: To apply exponential decay to an optimiser:
```julia ```julia
Optimiser(ExpDecay(..), Opt(..)) Optimiser(ExpDecay(..), Opt(..))
opt = Optimiser(ExpDecay(), ADAM())
opt = Optimiser(ExpDecay(), ADAM())
``` ```
""" """
mutable struct ExpDecay mutable struct ExpDecay
@ -510,7 +507,7 @@ function apply!(o::ExpDecay, x, Δ)
end end
""" """
WeightDecay(wd) WeightDecay(wd)
Decays the weight by `wd` Decays the weight by `wd`

36
src/optimise/train.jl
View File

@ -1,9 +1,22 @@
using Juno using Juno
import Zygote: Params, gradient import Zygote: Params, gradient
"""
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.
update!(x, x̄)
Update the array `x` according to `x .-= x̄`.
"""
function update!(x::AbstractArray, x̄) function update!(x::AbstractArray, x̄)
x .+= x̄ x .-= x̄
return x
end end
function update!(opt, x, x̄) function update!(opt, x, x̄)
@ -48,13 +61,14 @@ end
For each datapoint `d` in `data` computes the gradient of `loss(d...)` through For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
backpropagation and calls the optimizer `opt`. backpropagation and calls the optimizer `opt`.
In case datapoints `d` are of numeric array type, assumes no splatting is needed
and computes the gradient of `loss(d)`.
Takes a callback as keyword argument `cb`. For example, this will print "training" Takes a callback as keyword argument `cb`. For example, this will print "training"
every 10 seconds: every 10 seconds:
```julia train!(loss, params, data, opt,
Flux.train!(loss, params, data, opt, cb = throttle(() -> println("training"), 10))
cb = throttle(() -> println("training"), 10))
```
The callback can call `Flux.stop()` to interrupt the training loop. The callback can call `Flux.stop()` to interrupt the training loop.
@ -65,8 +79,14 @@ function train!(loss, ps, data, opt; cb = () -> ())
cb = runall(cb) cb = runall(cb)
@progress for d in data @progress for d in data
try try
gs = gradient(ps) do if d isa AbstractArray{<:Number}
loss(d...) gs = gradient(ps) do
loss(d)
end
else
gs = gradient(ps) do
loss(d...)
end
end end
update!(opt, ps, gs) update!(opt, ps, gs)
cb() cb()

2
src/utils.jl
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@ -60,7 +60,7 @@ head(x::Tuple) = reverse(Base.tail(reverse(x)))
squeezebatch(x) = reshape(x, head(size(x))) squeezebatch(x) = reshape(x, head(size(x)))
""" """
batch(xs) batch(xs)
Batch the arrays in `xs` into a single array. Batch the arrays in `xs` into a single array.

7
test/cuda/cuda.jl
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@ -58,6 +58,13 @@ end
@test y[3,:] isa CuArray @test y[3,:] isa CuArray
end 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() if CuArrays.has_cudnn()
@info "Testing Flux/CUDNN" @info "Testing Flux/CUDNN"
include("cudnn.jl") include("cudnn.jl")

91
test/data.jl
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@ -1,22 +1,85 @@
using Flux.Data @testset "DataLoader" begin
using Test 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 interaction with `train!`
@test MNIST.labels() isa Vector{Int64} 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 interaction with `train!`
@test FashionMNIST.labels() isa Vector{Int64} 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 length(CMUDict.phones()) == 39
@test size(Iris.features()) == (4,150)
@test Iris.labels() isa Vector{String} @test length(CMUDict.symbols()) == 84
@test size(Iris.labels()) == (150,) 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 size(Housing.features()) == (506, 13)
@test Housing.targets() isa Array{Float64}
@test size(Housing.targets()) == (506, 1)
end

15
test/layers/basic.jl
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@ -92,4 +92,19 @@ import Flux: activations
@test size(SkipConnection(Dense(10,10), (a,b) -> cat(a, b, dims = 2))(input)) == (10,4) @test size(SkipConnection(Dense(10,10), (a,b) -> cat(a, b, dims = 2))(input)) == (10,4)
end end
end end
@testset "output dimensions" begin
m = Chain(Conv((3, 3), 3 => 16), Conv((3, 3), 16 => 32))
@test Flux.outdims(m, (10, 10)) == (6, 6)
m = Dense(10, 5)
@test Flux.outdims(m, (5, 2)) == (5,)
@test Flux.outdims(m, (10,)) == (5,)
m = Flux.Diagonal(10)
@test Flux.outdims(m, (10,)) == (10,)
m = Maxout(() -> Conv((3, 3), 3 => 16), 2)
@test Flux.outdims(m, (10, 10)) == (8, 8)
end
end end

52
test/layers/conv.jl
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@ -107,3 +107,55 @@ end
true true
end end
end end
@testset "conv output dimensions" begin
m = Conv((3, 3), 3 => 16)
@test Flux.outdims(m, (10, 10)) == (8, 8)
m = Conv((3, 3), 3 => 16; stride = 2)
@test Flux.outdims(m, (5, 5)) == (2, 2)
m = Conv((3, 3), 3 => 16; stride = 2, pad = 3)
@test Flux.outdims(m, (5, 5)) == (5, 5)
m = Conv((3, 3), 3 => 16; stride = 2, pad = 3, dilation = 2)
@test Flux.outdims(m, (5, 5)) == (4, 4)
m = ConvTranspose((3, 3), 3 => 16)
@test Flux.outdims(m, (8, 8)) == (10, 10)
m = ConvTranspose((3, 3), 3 => 16; stride = 2)
@test Flux.outdims(m, (2, 2)) == (5, 5)
m = ConvTranspose((3, 3), 3 => 16; stride = 2, pad = 3)
@test Flux.outdims(m, (5, 5)) == (5, 5)
m = ConvTranspose((3, 3), 3 => 16; stride = 2, pad = 3, dilation = 2)
@test Flux.outdims(m, (4, 4)) == (5, 5)
m = DepthwiseConv((3, 3), 3 => 6)
@test Flux.outdims(m, (10, 10)) == (8, 8)
m = DepthwiseConv((3, 3), 3 => 6; stride = 2)
@test Flux.outdims(m, (5, 5)) == (2, 2)
m = DepthwiseConv((3, 3), 3 => 6; stride = 2, pad = 3)
@test Flux.outdims(m, (5, 5)) == (5, 5)
m = DepthwiseConv((3, 3), 3 => 6; stride = 2, pad = 3, dilation = 2)
@test Flux.outdims(m, (5, 5)) == (4, 4)
m = CrossCor((3, 3), 3 => 16)
@test Flux.outdims(m, (10, 10)) == (8, 8)
m = CrossCor((3, 3), 3 => 16; stride = 2)
@test Flux.outdims(m, (5, 5)) == (2, 2)
m = CrossCor((3, 3), 3 => 16; stride = 2, pad = 3)
@test Flux.outdims(m, (5, 5)) == (5, 5)
m = CrossCor((3, 3), 3 => 16; stride = 2, pad = 3, dilation = 2)
@test Flux.outdims(m, (5, 5)) == (4, 4)
m = MaxPool((2, 2))
@test Flux.outdims(m, (10, 10)) == (5, 5)
m = MaxPool((2, 2); stride = 1)
@test Flux.outdims(m, (5, 5)) == (4, 4)
m = MaxPool((2, 2); stride = 2, pad = 3)
@test Flux.outdims(m, (5, 5)) == (5, 5)
m = MeanPool((2, 2))
@test Flux.outdims(m, (10, 10)) == (5, 5)
m = MeanPool((2, 2); stride = 1)
@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

46
test/layers/normalisation.jl
View File

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

59
test/runtests.jl
View File

@ -1,32 +1,49 @@
using Flux, Test, Random, Statistics, Documenter using Flux
using Random using Flux.Data
using Test
using Random, Statistics, LinearAlgebra
using Documenter
using IterTools: ncycle
Random.seed!(0) Random.seed!(0)
@testset "Flux" begin @testset "Flux" begin
@info "Testing Basics" @testset "Utils" begin
include("utils.jl")
end
include("utils.jl") @testset "Onehot" begin
include("onehot.jl") include("onehot.jl")
include("optimise.jl") end
include("data.jl")
@info "Testing Layers" @testset "Optimise" begin
include("optimise.jl")
end
include("layers/basic.jl") @testset "Data" begin
include("layers/normalisation.jl") include("data.jl")
include("layers/stateless.jl") end
include("layers/conv.jl")
if Flux.use_cuda[] @testset "Layers" begin
include("cuda/cuda.jl") include("layers/basic.jl")
else include("layers/normalisation.jl")
@warn "CUDA unavailable, not testing GPU support" include("layers/stateless.jl")
end include("layers/conv.jl")
end
if VERSION >= v"1.2" @testset "CUDA" begin
doctest(Flux) if Flux.use_cuda[]
end include("cuda/cuda.jl")
else
@warn "CUDA unavailable, not testing GPU support"
end
end
end @testset "Docs" begin
if VERSION >= v"1.2"
doctest(Flux)
end
end
end # testset Flux