.github/pull_request_template.md

@@ -1,12 +0,0 @@
-[Please delete this text and describe your change here.
-For bugfixes, please detail the bug and include a test case which your patch fixes.
-If you are adding a new feature, please clearly describe the design, its rationale, the possible alternatives considered.
-It is easiest to merge new features when there is clear precedent in other systems; we need to know we're taking
-the right direction since it can be hard to change later.]
-
-### PR Checklist
-
-- [ ] Tests are added
-- [ ] Entry in NEWS.md
-- [ ] Documentation, if applicable
-- [ ] Final review from `@MikeInnes` or `@dhairyagandhi96` (for API changes).

.github/workflows/CompatHelper.yml

@@ -1,16 +0,0 @@
-name: CompatHelper
-
-on:
-  schedule:
-    - cron: '00 00 * * *'
-
-jobs:
-  CompatHelper:
-    runs-on: ubuntu-latest
-    steps:
-      - 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()'

.github/workflows/TagBot.yml

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

.gitlab-ci.yml

@@ -4,26 +4,26 @@ include:
 image: nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
 
 
-# julia:1.0:
-#   extends:
-#     - .julia:1.0
-#     - .test
-#   tags:
-#     - nvidia
-# 
-# julia:1.1:
-#   extends:
-#     - .julia:1.1
-#     - .test
-#   tags:
-#     - nvidia
-# 
-# julia:1.2:
-#   extends:
-#     - .julia:1.2
-#     - .test
-#   tags:
-#     - nvidia
+julia:1.0:
+  extends:
+    - .julia:1.0
+    - .test
+  tags:
+    - nvidia
+
+julia:1.1:
+  extends:
+    - .julia:1.1
+    - .test
+  tags:
+    - nvidia
+
+julia:1.2:
+  extends:
+    - .julia:1.2
+    - .test
+  tags:
+    - nvidia
 
 julia:1.3:
   extends:

.travis.yml

@@ -6,17 +6,19 @@ os:
   # - osx
 
 julia:
+  - 1.0
+  - 1.2
   - 1.3
-  - 1
   - nightly
 
-notifications:
-  email: false
+matrix:
+  allow_failures:
+    - julia: nightly
 
 jobs:
   include:
     - stage: "Documentation"
-      julia: 1.3
+      julia: 1.2
       os: linux
       script:
         - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));
@@ -24,9 +26,6 @@ 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()'

Manifest.toml

@@ -1,84 +1,76 @@
-# This file is machine-generated - editing it directly is not advised
-
 [[AbstractFFTs]]
 deps = ["LinearAlgebra"]
-git-tree-sha1 = "051c95d6836228d120f5f4b984dd5aba1624f716"
+git-tree-sha1 = "380e36c66edfa099cd90116b24c1ce8cafccac40"
 uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
-version = "0.5.0"
+version = "0.4.1"
 
 [[AbstractTrees]]
-deps = ["Markdown"]
-git-tree-sha1 = "33e450545eaf7699da1a6e755f9ea65f14077a45"
+deps = ["Markdown", "Test"]
+git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b"
 uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
-version = "0.3.3"
+version = "0.2.1"
 
 [[Adapt]]
 deps = ["LinearAlgebra"]
-git-tree-sha1 = "fd04049c7dd78cfef0b06cdc1f0f181467655712"
+git-tree-sha1 = "82dab828020b872fa9efd3abec1152b075bc7cbf"
 uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "1.1.0"
-
-[[ArrayLayouts]]
-deps = ["FillArrays", "LinearAlgebra"]
-git-tree-sha1 = "a504dca2ac7eda8761c8f7c1ed52427a1be75a3c"
-uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
-version = "0.2.6"
+version = "1.0.0"
 
 [[Base64]]
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 
+[[BinDeps]]
+deps = ["Compat", "Libdl", "SHA", "URIParser"]
+git-tree-sha1 = "12093ca6cdd0ee547c39b1870e0c9c3f154d9ca9"
+uuid = "9e28174c-4ba2-5203-b857-d8d62c4213ee"
+version = "0.8.10"
+
 [[BinaryProvider]]
-deps = ["Libdl", "Logging", "SHA"]
-git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058"
+deps = ["Libdl", "SHA"]
+git-tree-sha1 = "5b08ed6036d9d3f0ee6369410b830f8873d4024c"
 uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
-version = "0.5.10"
+version = "0.5.8"
 
 [[CEnum]]
-git-tree-sha1 = "1b77a77c3b28e0b3f413f7567c9bb8dd9bdccd14"
+git-tree-sha1 = "62847acab40e6855a9b5905ccb99c2b5cf6b3ebb"
 uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
-version = "0.3.0"
+version = "0.2.0"
 
 [[CUDAapi]]
 deps = ["Libdl", "Logging"]
-git-tree-sha1 = "831b825d10104bd29e28f6da93312a976830717b"
+git-tree-sha1 = "6eee47385c81ed3b3f716b745697869c712c2df3"
 uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
-version = "4.0.0"
+version = "2.0.0"
 
 [[CUDAdrv]]
 deps = ["CEnum", "CUDAapi", "Printf"]
-git-tree-sha1 = "f56bbf18c86bcff7a961a32a4947a5abb2963a29"
+git-tree-sha1 = "0f39fddace3324707469ace7fbcbc7b28d5cf921"
 uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
-version = "6.3.0"
+version = "4.0.4"
 
 [[CUDAnative]]
-deps = ["Adapt", "BinaryProvider", "CEnum", "CUDAapi", "CUDAdrv", "ExprTools", "GPUCompiler", "LLVM", "Libdl", "Pkg", "Printf"]
-git-tree-sha1 = "ac86db2b05fdfec96b011e25a504ffe7476e8a68"
+deps = ["Adapt", "CEnum", "CUDAapi", "CUDAdrv", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "Printf", "TimerOutputs"]
+git-tree-sha1 = "93f6c917ab2a9b5bb54f8f738f4ec1a6693cb716"
 uuid = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
-version = "3.1.0"
-
-[[CodeTracking]]
-deps = ["InteractiveUtils", "UUIDs"]
-git-tree-sha1 = "cab4da992adc0a64f63fa30d2db2fd8bec40cab4"
-uuid = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2"
-version = "0.5.11"
+version = "2.5.5"
 
 [[CodecZlib]]
-deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "ded953804d019afa9a3f98981d99b33e3db7b6da"
+deps = ["BinaryProvider", "Libdl", "TranscodingStreams"]
+git-tree-sha1 = "05916673a2627dd91b4969ff8ba6941bc85a960e"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.0"
+version = "0.6.0"
 
 [[ColorTypes]]
 deps = ["FixedPointNumbers", "Random"]
-git-tree-sha1 = "c73d9cfc2a9d8433dc77f5bff4bddf46b1d78c20"
+git-tree-sha1 = "10050a24b09e8e41b951e9976b109871ce98d965"
 uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
-version = "0.10.3"
+version = "0.8.0"
 
 [[Colors]]
-deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
-git-tree-sha1 = "1e9bba7984e78aa8cdeea7f9f7cc984ad4e4b1c7"
+deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Printf", "Reexport"]
+git-tree-sha1 = "c9c1845d6bf22e34738bee65c357a69f416ed5d1"
 uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
-version = "0.12.2"
+version = "0.9.6"
 
 [[CommonSubexpressions]]
 deps = ["Test"]
@@ -86,34 +78,36 @@ git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0"
 uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
 version = "0.2.0"
 
-[[CompilerSupportLibraries_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "7c4f882c41faa72118841185afc58a2eb00ef612"
-uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "0.3.3+0"
+[[Compat]]
+deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
+git-tree-sha1 = "ed2c4abadf84c53d9e58510b5fc48912c2336fbb"
+uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
+version = "2.2.0"
 
-[[Cthulhu]]
-deps = ["CodeTracking", "InteractiveUtils", "REPL", "UUIDs", "Unicode"]
-git-tree-sha1 = "f3643e78353199d3097821e806348bd83f364155"
-uuid = "f68482b8-f384-11e8-15f7-abe071a5a75f"
-version = "1.1.1"
+[[Conda]]
+deps = ["JSON", "VersionParsing"]
+git-tree-sha1 = "9a11d428dcdc425072af4aea19ab1e8c3e01c032"
+uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
+version = "1.3.0"
 
 [[CuArrays]]
-deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "Libdl", "LinearAlgebra", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SparseArrays", "Statistics", "TimerOutputs"]
-git-tree-sha1 = "1582b74d2322df7dd94549d4ac9d095e0f20e884"
+deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "Libdl", "LinearAlgebra", "MacroTools", "NNlib", "Printf", "Random", "Requires", "SparseArrays", "TimerOutputs"]
+git-tree-sha1 = "7e00178b18672ee2cf37244ac2a273b6b0701b04"
+repo-rev = "master"
+repo-url = "https://github.com/JuliaGPU/CuArrays.jl.git"
 uuid = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
-version = "2.2.1"
+version = "1.4.7"
 
 [[DataAPI]]
-git-tree-sha1 = "176e23402d80e7743fc26c19c681bfb11246af32"
+git-tree-sha1 = "674b67f344687a88310213ddfa8a2b3c76cc4252"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.3.0"
+version = "1.1.0"
 
 [[DataStructures]]
 deps = ["InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "af6d9c86e191c917c2276fbede1137e8ea20157f"
+git-tree-sha1 = "a1b652fb77ae8ca7ea328fa7ba5aa151036e5c10"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.17.17"
+version = "0.17.6"
 
 [[Dates]]
 deps = ["Printf"]
@@ -124,89 +118,79 @@ deps = ["Mmap"]
 uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 
 [[DiffResults]]
-deps = ["StaticArrays"]
-git-tree-sha1 = "da24935df8e0c6cf28de340b958f6aac88eaa0cc"
+deps = ["Compat", "StaticArrays"]
+git-tree-sha1 = "34a4a1e8be7bc99bc9c611b895b5baf37a80584c"
 uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
-version = "1.0.2"
+version = "0.0.4"
 
 [[DiffRules]]
 deps = ["NaNMath", "Random", "SpecialFunctions"]
-git-tree-sha1 = "eb0c34204c8410888844ada5359ac8b96292cfd1"
+git-tree-sha1 = "f734b5f6bc9c909027ef99f6d91d5d9e4b111eed"
 uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
-version = "1.0.1"
+version = "0.1.0"
 
 [[Distributed]]
-deps = ["Random", "Serialization", "Sockets"]
+deps = ["LinearAlgebra", "Random", "Serialization", "Sockets"]
 uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 
-[[ExprTools]]
-git-tree-sha1 = "6f0517056812fd6aa3af23d4b70d5325a2ae4e95"
-uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
-version = "0.1.1"
+[[FFTW]]
+deps = ["AbstractFFTs", "BinaryProvider", "Conda", "Libdl", "LinearAlgebra", "Reexport", "Test"]
+git-tree-sha1 = "6c5b420da0b8c12098048561b8d58f81adea506f"
+uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
+version = "1.0.1"
 
 [[FillArrays]]
 deps = ["LinearAlgebra", "Random", "SparseArrays"]
-git-tree-sha1 = "44f561e293987ffc84272cd3d2b14b0b93123d63"
+git-tree-sha1 = "1a9fe4e1323f38de0ba4da49eafd15b25ec62298"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "0.8.10"
+version = "0.8.2"
 
 [[FixedPointNumbers]]
-git-tree-sha1 = "3ba9ea634d4c8b289d590403b4a06f8e227a6238"
+git-tree-sha1 = "d14a6fa5890ea3a7e5dcab6811114f132fec2b4b"
 uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
-version = "0.8.0"
+version = "0.6.1"
 
 [[ForwardDiff]]
 deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
-git-tree-sha1 = "869540e4367122fbffaace383a5bdc34d6e5e5ac"
+git-tree-sha1 = "da46ac97b17793eba44ff366dc6cb70f1238a738"
 uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "0.10.10"
-
-[[Functors]]
-deps = ["MacroTools"]
-git-tree-sha1 = "f40adc6422f548176bb4351ebd29e4abf773040a"
-uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
-version = "0.1.0"
-
-[[Future]]
-deps = ["Random"]
-uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
+version = "0.10.7"
 
 [[GPUArrays]]
 deps = ["AbstractFFTs", "Adapt", "LinearAlgebra", "Printf", "Random", "Serialization"]
-git-tree-sha1 = "d887693eb1bd5e1fd573262a978745481895ec7d"
+git-tree-sha1 = "a0a3b927b1a06e63fb8b91950cc7df340b7d912c"
 uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
-version = "3.4.1"
-
-[[GPUCompiler]]
-deps = ["Cthulhu", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "TimerOutputs"]
-git-tree-sha1 = "5275aa268ecd09640b32560e1eae90c78816e4d1"
-uuid = "61eb1bfa-7361-4325-ad38-22787b887f55"
-version = "0.2.0"
+version = "2.0.0"
 
 [[IRTools]]
 deps = ["InteractiveUtils", "MacroTools", "Test"]
-git-tree-sha1 = "90ee39f9beaaa186e4968417ea2b8ed5673c91c0"
+git-tree-sha1 = "72421971e60917b8cd7737f9577c4f0f87eab306"
 uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
-version = "0.3.3"
+version = "0.3.0"
 
 [[InteractiveUtils]]
-deps = ["Markdown"]
+deps = ["LinearAlgebra", "Markdown"]
 uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 
+[[JSON]]
+deps = ["Dates", "Mmap", "Parsers", "Unicode"]
+git-tree-sha1 = "b34d7cef7b337321e97d22242c3c2b91f476748e"
+uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+version = "0.21.0"
+
 [[Juno]]
-deps = ["Base64", "Logging", "Media", "Profile"]
-git-tree-sha1 = "a686b0cf235fa3e491b79b4783c2d2382292b436"
+deps = ["Base64", "Logging", "Media", "Profile", "Test"]
+git-tree-sha1 = "30d94657a422d09cb97b6f86f04f750fa9c50df8"
 uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
-version = "0.8.2"
+version = "0.7.2"
 
 [[LLVM]]
 deps = ["CEnum", "Libdl", "Printf", "Unicode"]
-git-tree-sha1 = "dd3f584c3dbefe39b2a8fbafa1a3b77e31e21255"
+git-tree-sha1 = "74fe444b8b6d1ac01d639b2f9eaf395bcc2e24fc"
 uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
-version = "1.5.1"
+version = "1.3.2"
 
 [[LibGit2]]
-deps = ["Printf"]
 uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
 
 [[Libdl]]
@@ -220,10 +204,10 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
 
 [[MacroTools]]
-deps = ["Markdown", "Random"]
-git-tree-sha1 = "f7d2e3f654af75f01ec49be82c231c382214223a"
+deps = ["Compat", "DataStructures", "Test"]
+git-tree-sha1 = "82921f0e3bde6aebb8e524efc20f4042373c0c06"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.5"
+version = "0.5.2"
 
 [[Markdown]]
 deps = ["Base64"]
@@ -245,29 +229,30 @@ version = "0.4.3"
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[NNlib]]
-deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics"]
-git-tree-sha1 = "d9f196d911f55aeaff11b11f681b135980783824"
+deps = ["Libdl", "LinearAlgebra", "Requires", "Statistics", "TimerOutputs"]
+git-tree-sha1 = "0c667371391fc6bb31f7f12f96a56a17098b3de8"
 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.6.6"
+version = "0.6.0"
 
 [[NaNMath]]
 git-tree-sha1 = "928b8ca9b2791081dc71a51c55347c27c618760f"
 uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 version = "0.3.3"
 
-[[OpenSpecFun_jll]]
-deps = ["CompilerSupportLibraries_jll", "Libdl", "Pkg"]
-git-tree-sha1 = "d51c416559217d974a1113522d5919235ae67a87"
-uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
-version = "0.5.3+3"
-
 [[OrderedCollections]]
-git-tree-sha1 = "12ce190210d278e12644bcadf5b21cbdcf225cd3"
+deps = ["Random", "Serialization", "Test"]
+git-tree-sha1 = "c4c13474d23c60d20a67b217f1d7f22a40edf8f1"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.2.0"
+version = "1.1.0"
+
+[[Parsers]]
+deps = ["Dates", "Test"]
+git-tree-sha1 = "0139ba59ce9bc680e2925aec5b7db79065d60556"
+uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
+version = "0.3.10"
 
 [[Pkg]]
-deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
+deps = ["Dates", "LibGit2", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 
 [[Printf]]
@@ -293,10 +278,10 @@ uuid = "189a3867-3050-52da-a836-e630ba90ab69"
 version = "0.2.0"
 
 [[Requires]]
-deps = ["UUIDs"]
-git-tree-sha1 = "d37400976e98018ee840e0ca4f9d20baa231dc6b"
+deps = ["Test"]
+git-tree-sha1 = "f6fbf4ba64d295e146e49e021207993b6b48c7d1"
 uuid = "ae029012-a4dd-5104-9daa-d747884805df"
-version = "1.0.1"
+version = "0.5.2"
 
 [[SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
@@ -304,6 +289,10 @@ uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 [[Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 
+[[SharedArrays]]
+deps = ["Distributed", "Mmap", "Random", "Serialization"]
+uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
+
 [[Sockets]]
 uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 
@@ -318,16 +307,16 @@ deps = ["LinearAlgebra", "Random"]
 uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [[SpecialFunctions]]
-deps = ["OpenSpecFun_jll"]
-git-tree-sha1 = "d8d8b8a9f4119829410ecd706da4cc8594a1e020"
+deps = ["BinDeps", "BinaryProvider", "Libdl"]
+git-tree-sha1 = "3bdd374b6fd78faf0119b8c5d538788dbf910c6e"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "0.10.3"
+version = "0.8.0"
 
 [[StaticArrays]]
 deps = ["LinearAlgebra", "Random", "Statistics"]
-git-tree-sha1 = "5c06c0aeb81bef54aed4b3f446847905eb6cbda0"
+git-tree-sha1 = "5a3bcb6233adabde68ebc97be66e95dcb787424c"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "0.12.3"
+version = "0.12.1"
 
 [[Statistics]]
 deps = ["LinearAlgebra", "SparseArrays"]
@@ -335,9 +324,9 @@ uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [[StatsBase]]
 deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
-git-tree-sha1 = "a6102b1f364befdb05746f386b67c6b7e3262c45"
+git-tree-sha1 = "c53e809e63fe5cf5de13632090bc3520649c9950"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.33.0"
+version = "0.32.0"
 
 [[Test]]
 deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
@@ -345,9 +334,9 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[TimerOutputs]]
 deps = ["Printf"]
-git-tree-sha1 = "f458ca23ff80e46a630922c555d838303e4b9603"
+git-tree-sha1 = "311765af81bbb48d7bad01fb016d9c328c6ede03"
 uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
-version = "0.5.6"
+version = "0.5.3"
 
 [[TranscodingStreams]]
 deps = ["Random", "Test"]
@@ -355,30 +344,36 @@ git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
 uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
 version = "0.9.5"
 
+[[URIParser]]
+deps = ["Test", "Unicode"]
+git-tree-sha1 = "6ddf8244220dfda2f17539fa8c9de20d6c575b69"
+uuid = "30578b45-9adc-5946-b283-645ec420af67"
+version = "0.4.0"
+
 [[UUIDs]]
-deps = ["Random", "SHA"]
+deps = ["Random"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
 [[Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
 
-[[ZipFile]]
-deps = ["Libdl", "Printf", "Zlib_jll"]
-git-tree-sha1 = "254975fef2fc526583bb9b7c9420fe66ffe09f2f"
-uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.9.2"
+[[VersionParsing]]
+deps = ["Compat"]
+git-tree-sha1 = "c9d5aa108588b978bd859554660c8a5c4f2f7669"
+uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
+version = "1.1.3"
 
-[[Zlib_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "a2e0d558f6031002e380a90613b199e37a8565bf"
-uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
-version = "1.2.11+10"
+[[ZipFile]]
+deps = ["BinaryProvider", "Libdl", "Printf"]
+git-tree-sha1 = "580ce62b6c14244916cc28ad54f8a2e2886f843d"
+uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
+version = "0.8.3"
 
 [[Zygote]]
-deps = ["AbstractFFTs", "ArrayLayouts", "DiffRules", "FillArrays", "ForwardDiff", "Future", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "707ceea58e2bd0ff3077ab13a92f8355181d3ee4"
+deps = ["DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
+git-tree-sha1 = "e4245b9c5362346e154b62842a89a18e0210b92b"
 uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.4.20"
+version = "0.4.1"
 
 [[ZygoteRules]]
 deps = ["MacroTools"]

NEWS.md

@@ -1,19 +1,3 @@
-# v0.11
-* Change to `DataLoader`'s constructor [https://github.com/FluxML/Flux.jl/pull/1152]
-* Use `DataLoader` with `NamedTuple`s, so that tensors can be accessed by name [https://github.com/FluxML/Flux.jl/pull/1221].
-* Error if Dense layers weights and biases are not arrays [https://github.com/FluxML/Flux.jl/pull/1218].
-
-# 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()`.
-* Added option to set `bias` to [Flux.Zeros](https://github.com/FluxML/Flux.jl/pull/873) to eliminating `bias` from being trained.
-* Added `GlobalMaxPool` and `GlobalMeanPool` [layers](https://github.com/FluxML/Flux.jl/pull/950) for performing global pooling operations.
-* Added `ClipValue` and `ClipNorm` in this [pr](https://github.com/FluxML/Flux.jl/pull/1133) to `Flux.Optimise` to provide a cleaner API for gradient clipping.
-* Added new kwarg-only [constructors](https://github.com/FluxML/Flux.jl/pull/873) for the various convolutional layers.
-* Documented the convolutional layer constructors accepting `weight` and `bias` keyword arguments to supply custom arrays for those fields.
-* Testing suite improvements now test for gradients of all layers along with GPU support.
-* Functors have now moved to [Functors.jl](https://github.com/FluxML/Flux.jl/pull/1174) to allow for their use outside of Flux.
-* Added [helper functions](https://github.com/FluxML/Flux.jl/pull/873) `Flux.convfilter` and `Flux.depthwiseconvfilter` to construct weight arrays for convolutions outside of layer constructors so as to not have to depend on the default layers for custom implementations.
-
 # 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.

Project.toml

@@ -1,6 +1,6 @@
 name = "Flux"
 uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-version = "0.11.0-DEV"
+version = "0.10.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -9,9 +9,7 @@ CodecZlib = "944b1d66-785c-5afd-91f1-9de20f533193"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 CuArrays = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 Juno = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
@@ -26,26 +24,23 @@ ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-AbstractTrees = "0.2, 0.3"
-Adapt = "1, 2.0"
-CodecZlib = "0.5, 0.6, 0.7"
-Colors = "0.8, 0.9, 0.10, 0.11, 0.12"
-CuArrays = "2"
-Functors = "0.1"
-Juno = "0.5, 0.6, 0.7, 0.8"
+AbstractTrees = "0.2"
+Adapt = "1"
+CodecZlib = "0.5, 0.6"
+Colors = "0.8, 0.9"
+CuArrays = "1.4.3"
+Juno = "0.5, 0.6, 0.7"
 MacroTools = "0.3, 0.4, 0.5"
 NNlib = "0.6"
 Reexport = "0.2"
 StatsBase = "0"
-ZipFile = "0.7, 0.8, 0.9"
-Zygote = "0.4.13"
-julia = "1.3"
+ZipFile = "0.7, 0.8"
+Zygote = "0.4"
+julia = "1"
 
 [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", "IterTools", "LinearAlgebra"]
+test = ["Test", "Documenter"]

README.md

@@ -12,4 +12,4 @@ Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack, a
 
 See the [documentation](https://fluxml.github.io/Flux.jl/) or the [model zoo](https://github.com/FluxML/model-zoo/) for examples.
 
-If you use Flux in your research, please [cite](CITATION.bib) our work.
+If you use Flux in research, please see [our papers](CITATION.bib) for appropriate citations.

bors.toml

@@ -1,4 +1,4 @@
 status = [
-  "ci/gitlab%"
+  "ci/gitlab/%"
 ]
-timeout-sec = 7200
+timeout-sec = 14400

docs/Manifest.toml

@@ -0,0 +1,89 @@
+# This file is machine-generated - editing it directly is not advised
+
+[[Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+
+[[Dates]]
+deps = ["Printf"]
+uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
+
+[[Distributed]]
+deps = ["Random", "Serialization", "Sockets"]
+uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+
+[[DocStringExtensions]]
+deps = ["LibGit2", "Markdown", "Pkg", "Test"]
+git-tree-sha1 = "0513f1a8991e9d83255e0140aace0d0fc4486600"
+uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
+version = "0.8.0"
+
+[[Documenter]]
+deps = ["Base64", "DocStringExtensions", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "REPL", "Test", "Unicode"]
+git-tree-sha1 = "c61d6eedbc3c4323c08b64af12d29c8ee0fcbb5f"
+uuid = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+version = "0.23.2"
+
+[[InteractiveUtils]]
+deps = ["Markdown"]
+uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+
+[[JSON]]
+deps = ["Dates", "Mmap", "Parsers", "Unicode"]
+git-tree-sha1 = "b34d7cef7b337321e97d22242c3c2b91f476748e"
+uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+version = "0.21.0"
+
+[[LibGit2]]
+uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
+
+[[Logging]]
+uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
+
+[[Markdown]]
+deps = ["Base64"]
+uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
+
+[[Mmap]]
+uuid = "a63ad114-7e13-5084-954f-fe012c677804"
+
+[[Parsers]]
+deps = ["Dates", "Test"]
+git-tree-sha1 = "db2b35dedab3c0e46dc15996d170af07a5ab91c9"
+uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
+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"

docs/Project.toml

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

docs/make.jl

@@ -1,36 +1,29 @@
+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",
-                     "Advanced Model Building" => "models/advanced.md",
-                     "NNlib" => "models/nnlib.md"],
-                  "Handling Data" =>
-                    ["One-Hot Encoding" => "data/onehot.md",
-                     "DataLoader" => "data/dataloader.md"],
+                     "Model Reference" => "models/layers.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(
-             analytics = "UA-36890222-9",
-             assets = ["assets/flux.css"],
-             prettyurls = get(ENV, "CI", nothing) == "true"),
-         )
+         format = Documenter.HTML(assets = ["assets/flux.css"],
+                                  analytics = "UA-36890222-9",
+                                  prettyurls = haskey(ENV, "CI")))
 
-deploydocs(repo = "github.com/FluxML/Flux.jl.git",
-           target = "build",
-           push_preview = true)
+deploydocs(repo = "github.com/FluxML/Flux.jl.git")

docs/src/data/dataloader.md

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

docs/src/data/onehot.md

@@ -7,15 +7,15 @@ julia> using Flux: onehot, onecold
 
 julia> onehot(:b, [:a, :b, :c])
 3-element Flux.OneHotVector:
- 0
- 1
- 0
+ false
+  true
+ false
 
 julia> onehot(:c, [:a, :b, :c])
 3-element Flux.OneHotVector:
- 0
- 0
- 1
+ false
+ false
+  true
 ```
 
 The inverse is `onecold` (which can take a general probability distribution, as well as just booleans).
@@ -31,11 +31,6 @@ 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.
@@ -57,7 +52,3 @@ 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
-```

docs/src/datasets.md

@@ -1,20 +0,0 @@
-# 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()
-```
-

docs/src/ecosystem.md

@@ -1,21 +0,0 @@
-# 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.

docs/src/gpu.md

@@ -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 # CuArray
+d.W # Tracked 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)
-5-element CuArray{Float32,1}:
+Tracked 5-element CuArray{Float32,1}:
  -0.30535
  ⋮
  -0.618002

docs/src/models/advanced.md

@@ -1,73 +0,0 @@
-# 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,)
-
-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) 
-```
-

docs/src/models/basics.md

@@ -32,6 +32,8 @@ julia> gradient(f, [2, 1], [2, 0])
 But machine learning models can have *hundreds* of parameters! To handle this, Flux lets you work with collections of parameters, via `params`. You can get the gradient of all parameters used in a program without explicitly passing them in.
 
 ```jldoctest basics
+julia> using Flux
+
 julia> x = [2, 1];
 
 julia> y = [2, 0];
@@ -67,8 +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
@@ -217,26 +219,3 @@ 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.
-
-`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
-Flux.outdims
-```

docs/src/models/layers.md

@@ -14,17 +14,10 @@ These layers are used to build convolutional neural networks (CNNs).
 ```@docs
 Conv
 MaxPool
-GlobalMaxPool
 MeanPool
-GlobalMeanPool
 DepthwiseConv
 ConvTranspose
 CrossCor
-SamePad
-flatten
-Flux.Zeros
-Flux.convfilter
-Flux.depthwiseconvfilter
 ```
 
 ## Recurrent Layers
@@ -36,7 +29,6 @@ RNN
 LSTM
 GRU
 Flux.Recur
-Flux.reset!
 ```
 
 ## Other General Purpose Layers
@@ -48,45 +40,28 @@ 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
-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
-```

docs/src/models/nnlib.md

@@ -1,61 +0,0 @@
-# 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
-```

docs/src/models/regularisation.md

@@ -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
+26.01749952921026 (tracked)
 ```
 
 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))
@@ -64,7 +64,3 @@ julia> activations(c, rand(10))
 julia> sum(norm, ans)
 2.1166067f0
 ```
-
-```@docs
-Flux.activations
-```

docs/src/performance.md

@@ -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`).
@@ -14,8 +14,7 @@ Which means allocations occur much faster.
 And you use less memory.
 
 
-## Preserve inputs' types
-
+## Make sure your activation and loss functions preserve the type of their inputs
 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.
 
@@ -30,29 +29,31 @@ 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) = 0.01*x + tanh(x)
+    leaky_tanh(x) = 0.01x + tanh(x)
 ```
 
-While one could change the activation function (e.g. to use `0.01f0*x`), the idiomatic (and safe way)  to avoid type casts whenever inputs changes is to use `oftype`:
+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`.
+
 ```
-    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
+## Evaluate batches as Matrices of features, rather than sequences of Vector 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

docs/src/training/optimisers.md

@@ -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.Optimise: update!
+using Flux: update!
 
 η = 0.1 # Learning Rate
 for p in (W, b)
@@ -46,13 +46,11 @@ 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
@@ -63,7 +61,7 @@ ADAMW
 
 ## Optimiser Interface
 
-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.
+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.
 
 In this manner Flux also allows one to create custom optimisers to be used seamlessly. Let's work this with a simple example.
 
@@ -80,7 +78,7 @@ Momentum(eta::Real, rho::Real) = Momentum(eta, rho, IdDict())
 The `Momentum` type will act as our optimiser in this case. Notice that we have added all the parameters as fields, along with the velocity which we will use as our state dictionary. Each parameter in our models will get an entry in there. We can now define the rule applied when this optimiser is invoked.
 
 ```julia
-function Flux.Optimise.apply!(o::Momentum, x, Δ)
+function apply!(o::Momentum, x, Δ)
   η, ρ = o.eta, o.rho
   v = get!(o.velocity, x, zero(x))::typeof(x)
   @. v = ρ * v - η * Δ
@@ -101,15 +99,15 @@ Flux internally calls on this function via the `update!` function. It shares the
 
 ## Composing Optimisers
 
-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
+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
 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` optimiser. The defaults of `ExpDecay` say that its learning rate will be decayed every 1000 steps.
-It is then applied like any optimiser.
+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.
 
 ```julia
 w = randn(10, 10)
@@ -140,16 +138,3 @@ ExpDecay
 InvDecay
 WeightDecay
 ```
-
-## Gradient Clipping
-
-Gradient clipping is useful for training recurrent neural networks, which have a tendency to suffer from the exploding gradient problem. An example usage is
-
-```julia
-opt = Optimiser(ClipValue(1e-3), ADAM(1e-3))
-```
-
-```@docs
-ClipValue
-ClipNorm
-```

docs/src/training/training.md

@@ -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 `train!`:
+With these we can call `Flux.train!`:
 
-```@docs
-Flux.Optimise.train!
+```julia
+Flux.train!(objective, params, data, opt)
 ```
 
 There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo).
@@ -32,7 +32,6 @@ 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.
 
@@ -42,8 +41,6 @@ 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:
@@ -59,8 +56,7 @@ data = [(x, y)]
 ```julia
 data = [(x, y), (x, y), (x, y)]
 # Or equivalently
-using IterTools: ncycle
-data = ncycle([(x, y)], 3)
+data = Iterators.repeated((x, y), 3)
 ```
 
 It's common to load the `x`s and `y`s separately. In this case you can use `zip`:
@@ -71,14 +67,6 @@ 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`.
 
@@ -95,10 +83,6 @@ 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:
@@ -126,30 +110,3 @@ cb = function ()
   accuracy() > 0.9 && Flux.stop()
 end
 ```
-
-## Custom Training loops
-
-The `Flux.train!` function can be very convenient, especially for simple problems.
-Its also very flexible with the use of callbacks.
-But for some problems its much cleaner to write your own custom training loop.
-An example follows that works similar to the default `Flux.train` but with no callbacks.
-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
-    gs = gradient(ps) do
-      training_loss = loss(d...)
-      # Insert whatever code you want here that needs Training loss, e.g. logging
-      return training_loss
-    end
-    # insert what ever code you want here that needs gradient
-    # E.g. logging with TensorBoardLogger.jl as histogram so you can see if it is becoming huge
-    update!(opt, ps, gs)
-    # Here you might like to check validation set accuracy, and break out to do early stopping
-  end
-end
-```
-You could simplify this further, for example by hard-coding in the loss function.

docs/src/utilities.md

@@ -1,49 +0,0 @@
-# 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
-```

src/Flux.jl

@@ -3,33 +3,28 @@ module Flux
 # Zero Flux Given
 
 using Base: tail
-using Statistics, Random, LinearAlgebra
-using Zygote, MacroTools, Juno, Reexport
+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, SamePad, Conv, CrossCor, ConvTranspose,
-       GlobalMaxPool, GlobalMeanPool, MaxPool, MeanPool, flatten,
+export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,
        DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm,
-       SkipConnection, params, fmap, cpu, gpu, f32, f64, testmode!, trainmode!
+       SkipConnection, params, fmap, cpu, gpu, f32, f64
 
 include("optimise/Optimise.jl")
 using .Optimise
 using .Optimise: @epochs
-export Descent, ADAM, Momentum, Nesterov, RMSProp,
+export SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
   ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM,
-  ADAMW, RADAM, InvDecay, ExpDecay, WeightDecay,
-  ClipValue, ClipNorm
+  ADAMW, RADAM, InvDecay, ExpDecay, WeightDecay
 
 
 using CuArrays
 const use_cuda = Ref(false)
 
 include("utils.jl")
-include("zeros.jl")
 include("onehot.jl")
 include("functor.jl")
 
@@ -43,13 +38,24 @@ include("data/Data.jl")
 
 include("deprecations.jl")
 
-include("cuda/cuda.jl")
-
 function __init__()
-  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."
+  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."
     end
   end
 end

src/cuda/curnn.jl

@@ -1,5 +1,6 @@
 import ..Flux: Flux, relu
 using CuArrays.CUDAnative
+using CuArrays: @cuindex, cudims
 
 CuRNN{T} = Flux.RNNCell{<:Union{typeof(tanh),typeof(relu)},<:CuArray{T,2},<:CuArray{T,1}}
 CuGRU{T} = Flux.GRUCell{<:CuArray{T,2},<:CuArray{T,1}}

src/data/Data.jl

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

src/data/cmudict.jl

@@ -24,35 +24,18 @@ 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
@@ -61,14 +44,6 @@ 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'..., '_', '-', '.']

src/data/dataloader.jl

@@ -1,110 +0,0 @@
-# Adapted from Knet's src/data.jl (author: Deniz Yuret)
-
-struct DataLoader{D}
-    data::D
-    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 a single data tensor, or a tuple (or a named tuple) of tensors.
-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 in the `data` field of the DataLoader. 
-
-Usage example:
-
-    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
-
-    # similar, but yielding tuples
-    train_loader = DataLoader((Xtrain,), batchsize=2) 
-    for (x,) in train_loader
-        @assert size(x) == (10, 2)
-        ...
-    end
-
-    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)
-
-    # can use NamedTuple to name tensors
-    train_loader = DataLoader((images=Xtrain, labels=Ytrain), batchsize=2, shuffle=true)
-    for datum in train_loader
-        @assert size(datum.images) == (10, 2)
-        @assert size(datum.labels) == (2,)
-    end
-"""
-function DataLoader(data; batchsize=1, shuffle=false, partial=true)
-    batchsize > 0 || throw(ArgumentError("Need positive batchsize"))
-    
-    n = _nobs(data) 
-    if n < batchsize
-        @warn "Number of observations less than batchsize, decreasing the batchsize to $n"
-        batchsize = n
-    end
-    imax = partial ? n : n - batchsize + 1
-    DataLoader(data, batchsize, n, partial, imax, [1:n;], shuffle)
-end
-
-@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]
-    batch = _getobs(d.data, ids)
-    return (batch, nexti)
-end
-
-function Base.length(d::DataLoader)
-    n = d.nobs / d.batchsize
-    d.partial ? ceil(Int,n) : floor(Int,n)
-end
-
-_nobs(data::AbstractArray) = size(data)[end]
-
-function _nobs(data::Union{Tuple, NamedTuple})
-    length(data) > 0 || throw(ArgumentError("Need at least one data input"))
-    n = _nobs(data[1])
-    if !all(x -> _nobs(x) == n, Base.tail(data))
-        throw(DimensionMismatch("All data should contain same number of observations"))
-    end
-    return n
-end
-
-_getobs(data::AbstractArray, i) = data[ntuple(i -> Colon(), Val(ndims(data) - 1))..., i]
-_getobs(data::Union{Tuple, NamedTuple}, i) = map(Base.Fix2(_getobs, i), data)
-
-Base.eltype(::DataLoader{D}) where D = D

src/data/fashion-mnist.jl

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

src/data/housing.jl

@@ -1,136 +0,0 @@
-"""
-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

src/data/iris.jl

@@ -2,12 +2,13 @@
 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
 
@@ -27,12 +28,15 @@ function load()
 end
 
 """
+
     labels()
 
 Get the labels of the iris dataset, a 150 element array of strings listing the
 species of each example.
 
-```jldoctest; setup = :(Flux.Data.Iris.load())
+```jldoctest
+julia> using Flux
+
 julia> labels = Flux.Data.Iris.labels();
 
 julia> summary(labels)
@@ -49,13 +53,16 @@ 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; setup = :(Flux.Data.Iris.load())
+```jldoctest
+julia> using Flux
+
 julia> features = Flux.Data.Iris.features();
 
 julia> summary(features)

src/data/mnist.jl

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

src/data/sentiment.jl

@@ -1,4 +1,3 @@
-"Stanford Sentiment Treebank dataset."
 module Sentiment
 
 using ZipFile
@@ -40,28 +39,8 @@ 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

src/functor.jl

@@ -1,41 +1,44 @@
 import Adapt: adapt, adapt_storage
 using Zygote: IdSet
-import Functors: @functor, functor, fmap
+
+functor(x) = (), _ -> x
+
+functor(x::Tuple) = x, y -> y
+functor(x::NamedTuple) = x, y -> y
+
+functor(x::AbstractArray) = x, y -> y
+functor(x::AbstractArray{<:Number}) = (), _ -> x
+
+function makefunctor(m::Module, T, fs = fieldnames(T))
+  @eval m begin
+    Flux.functor(x::$T) = ($([:($f=x.$f) for f in fs]...),), y -> $T(y...)
+  end
+end
+
+function functorm(T, fs = nothing)
+  fs == nothing || isexpr(fs, :tuple) || error("@functor T (a, b)")
+  fs = fs == nothing ? [] : [:($(map(QuoteNode, fs.args)...),)]
+  :(makefunctor(@__MODULE__, $(esc(T)), $(fs...)))
+end
+
+macro functor(args...)
+  functorm(args...)
+end
+
+isleaf(x) = functor(x)[1] === ()
+
+function fmap1(f, x)
+  func, re = functor(x)
+  re(map(f, func))
+end
+
+function fmap(f, x; cache = IdDict())
+  haskey(cache, x) && return cache[x]
+  cache[x] = isleaf(x) ? f(x) : fmap1(x -> fmap(f, x, cache = cache), x)
+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())

src/layers/basic.jl

@@ -4,23 +4,17 @@
 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
@@ -30,7 +24,7 @@ end
 @forward Chain.layers Base.getindex, Base.length, Base.first, Base.last,
   Base.iterate, Base.lastindex
 
-functor(::Type{<:Chain}, c) = c.layers, ls -> Chain(ls...)
+functor(c::Chain) = c.layers, ls -> Chain(ls...)
 
 applychain(::Tuple{}, x) = x
 applychain(fs::Tuple, x) = applychain(tail(fs), first(fs)(x))
@@ -39,25 +33,12 @@ 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, ", ")
   print(io, ")")
 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
 # it might be replaced in the future for better performance
@@ -66,7 +47,6 @@ 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)
@@ -85,24 +65,24 @@ extraChain(::Tuple{}, x) = ()
 """
     Dense(in::Integer, out::Integer, σ = identity)
 
-Create a traditional `Dense` layer with parameters `W` and `b`.
+Creates 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`.
 
-# Examples
-```jldoctest; setup = :(using Random; Random.seed!(0))
+```julia
 julia> d = Dense(5, 2)
 Dense(5, 2)
 
 julia> d(rand(5))
-2-element Array{Float32,1}:
-  -0.16210233
-   0.12311903```
+Tracked 2-element Array{Float64,1}:
+  0.00257447
+  -0.00449443
+```
 """
-struct Dense{F,S<:AbstractArray,T<:AbstractArray}
+struct Dense{F,S,T}
   W::S
   b::T
   σ::F
@@ -136,23 +116,10 @@ end
 (a::Dense{<:Any,W})(x::AbstractArray{<:AbstractFloat}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
   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)
 
-Create an element-wise linear transformation layer with learnable
+Creates an element-wise linear transformation layer with learnable
 vectors `α` and `β`:
 
     y = α .* x .+ β
@@ -178,16 +145,22 @@ function Base.show(io::IO, l::Diagonal)
   print(io, "Diagonal(", length(l.α), ")")
 end
 
-outdims(l::Diagonal, isize) = (length(l.α),)
 
 """
     Maxout(over)
 
-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` 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.
 
 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
@@ -196,18 +169,17 @@ end
 """
     Maxout(f, n_alts)
 
-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.
+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.
 
-# Examples
-
-This constructs a `Maxout` layer over 4 internal dense linear layers, each
-identical in structure (784 inputs, 128 outputs):
+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).
 ```julia
-insize = 784
-outsize = 128
-Maxout(()->Dense(insize, outsize), 4)
+    insize = 784
+    outsize = 128
+    Maxout(()->Dense(insize, outsize), 4)
 ```
 """
 function Maxout(f, n_alts)
@@ -221,21 +193,17 @@ function (mo::Maxout)(input::AbstractArray)
     mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over)
 end
 
-outdims(l::Maxout, isize) = outdims(first(l.over), isize)
-
 """
-    SkipConnection(layer, connection)
+    SkipConnection(layers, connection)
 
-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.
+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.
 
-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)

src/layers/conv.jl

@@ -1,66 +1,27 @@
-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])
+using NNlib: conv, ∇conv_data, depthwiseconv
 
 expand(N, i::Tuple) = i
 expand(N, i::Integer) = ntuple(_ -> i, N)
-
 """
-    SamePad
+    Conv(size, in=>out)
+    Conv(size, in=>out, relu)
 
-Padding for convolutional layers will be calculated so that outputshape == inputshape when stride = 1.
+Standard convolutional layer. `size` should be a tuple like `(2, 2)`.
+`in` and `out` specify the number of input and output channels respectively.
 
-For stride > 1 the output shape depends on the type of convolution layer.
-"""
-struct SamePad end
+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.
 
-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)
+    size = (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).
+Data should be stored in WHCN order (width, height, # channels, # batches).
 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.
-
-# 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
@@ -71,68 +32,25 @@ struct Conv{N,M,F,A,V}
   dilation::NTuple{N,Int}
 end
 
-"""
-    Conv(weight::AbstractArray, bias::AbstractArray)
-    Conv(weight::AbstractArray, bias::AbstractArray, activation)
-
-Constructs the convolutional layer with user defined weight and bias arrays.
-
-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;
+function Conv(w::AbstractArray{T,N}, b::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
 
-function Conv(;weight::AbstractArray{T,N}, bias::Union{Zeros, AbstractVector{T}},
-              activation = identity, stride = 1, pad = 0, dilation = 1) where {T,N}
-  Conv(weight, bias, activation, stride = stride, pad = pad, dilation = dilation)
-end
-
-"""
-    convfilter(filter::Tuple, in=>out)
-
-Constructs a standard convolutional weight matrix with given `filter` and
-channels from `in` to `out`.
-
-Accepts the keyword `init` (default: `glorot_uniform`) to control the sampling
-distribution.
-
-See also: [`depthwiseconvfilter`](@ref)
-"""
-convfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
-          init = glorot_uniform) where N = init(filter..., ch...)
-
-function Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
-            init = glorot_uniform,  stride = 1, pad = 0, dilation = 1,
-            weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N
-
-  Conv(weight, bias, σ,
-      stride = stride, pad = pad, dilation = dilation)
-end
+Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
+     init = glorot_uniform,  stride = 1, pad = 0, dilation = 1) where N =
+  Conv(init(k..., ch...), zeros(ch[2]), σ,
+       stride = stride, pad = pad, dilation = dilation)
 
 @functor Conv
 
 function (c::Conv)(x::AbstractArray)
   # TODO: breaks gpu broadcast :(
   # ndims(x) == ndims(c.weight)-1 && return squeezebatch(c(reshape(x, size(x)..., 1)))
-  σ, b = c.σ, reshape(c.bias, ntuple(_->1, length(c.stride))..., :, 1)
+  σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
   cdims = DenseConvDims(x, c.weight; stride=c.stride, padding=c.pad, dilation=c.dilation)
   σ.(conv(x, c.weight, cdims) .+ b)
 end
@@ -151,38 +69,16 @@ end
   a(T.(x))
 
 """
-    outdims(l::Conv, isize::Tuple)
+    ConvTranspose(size, in=>out)
+    ConvTranspose(size, in=>out, relu)
 
-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)
-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(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)`.
+Standard convolutional transpose layer. `size` 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.
-
-Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
-Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
+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.
 
 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
@@ -193,39 +89,18 @@ struct ConvTranspose{N,M,F,A,V}
   dilation::NTuple{N,Int}
 end
 
-"""
-    ConvTranspose(weight::AbstractArray, bias::AbstractArray)
-    ConvTranspose(weight::AbstractArray, bias::AbstractArray, activation)
-
-Constructs the convolutional transpose layer with user defined weight and bias arrays.
-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}
+function ConvTranspose(w::AbstractArray{T,N}, b::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
 
-function ConvTranspose(;weight::AbstractArray{T,N}, bias::Union{Zeros, AbstractVector{T}},
-                        activation = identity, stride = 1, pad = 0, dilation = 1) where {T,N}
-  ConvTranspose(weight, bias, activation, stride = stride, pad = pad, dilation = dilation)
-end
-
-function ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
-                      init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
-                      weight = convfilter(k, reverse(ch), init = init), bias = zeros(ch[2])) where N
-
-  ConvTranspose(weight, bias, σ,
+ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
+              init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N =
+ConvTranspose(init(k..., reverse(ch)...), zeros(ch[2]), σ,
               stride = stride, pad = pad, dilation = dilation)
-end
 
 @functor ConvTranspose
 
@@ -237,9 +112,9 @@ function conv_transpose_dims(c::ConvTranspose, x::AbstractArray)
     batch_size = size(x)[end]
     # Create DenseConvDims() that looks like the corresponding conv()
     return DenseConvDims((I..., C_in, batch_size), size(c.weight);
-                        stride=c.stride,
-                        padding=c.pad,
-                        dilation=c.dilation,
+        stride=c.stride,
+        padding=c.pad,
+        dilation=c.dilation,
     )
 end
 
@@ -250,7 +125,7 @@ function (c::ConvTranspose)(x::AbstractArray)
   # ndims(x) == ndims(c.weight)-1 && return squeezebatch(c(reshape(x, size(x)..., 1)))
   σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
   cdims = conv_transpose_dims(c, x)
-  σ.(∇conv_data(x, c.weight, cdims) .+ b)
+  return σ.(∇conv_data(x, c.weight, cdims) .+ b)
 end
 
 function Base.show(io::IO, l::ConvTranspose)
@@ -265,28 +140,18 @@ end
 
 (a::ConvTranspose{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
   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(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)
+    DepthwiseConv(size, in=>out)
+    DepthwiseConv(size, in=>out, relu)
 
-Depthwise convolutional layer. `filter` should be a tuple like `(2, 2)`.
+Depthwise convolutional layer. `size` 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 (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.
+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.
 
 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
@@ -297,54 +162,20 @@ struct DepthwiseConv{N,M,F,A,V}
   dilation::NTuple{N,Int}
 end
 
-"""
-    DepthwiseConv(weight::AbstractArray, bias::AbstractArray)
-    DepthwiseConv(weight::AbstractArray, bias::AbstractArray, activation)
-
-Constructs the `DepthwiseConv` layer with user defined weight and bias arrays.
-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}
+function DepthwiseConv(w::AbstractArray{T,N}, b::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
 
-function DepthwiseConv(;weight::AbstractArray{T,N}, bias::Union{Zeros, AbstractVector{T}},
-                      activation = identity, stride = 1, pad = 0, dilation = 1) where {T,N}
-  DepthwiseConv(weight, bias, activation, stride = stride, pad = pad, dilation = dilation)
-end
-
-"""
-    depthwiseconvfilter(filter::Tuple, in=>out)
-
-Constructs a depthwise convolutional weight array defined by `filter` and channels
-from `in` to `out`.
-
-Accepts the keyword `init` (default: `glorot_uniform`) to control the sampling
-distribution.
-
-See also: [`convfilter`](@ref)
-"""
-depthwiseconvfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
-                    init = glorot_uniform) where N = init(filter..., div(ch[2], ch[1]), ch[1])
-
 function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
-                      init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
-                      weight = depthwiseconvfilter(k, ch, init = init), bias = zeros(ch[2])) where N
+     init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N
   @assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels"
-
   return DepthwiseConv(
-    weight,
-    bias,
+    init(k..., div(ch[2], ch[1]), ch[1]),
+    zeros(ch[2]),
     σ;
     stride = stride,
     pad = pad,
@@ -373,38 +204,26 @@ end
 (a::DepthwiseConv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
   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(filter, in=>out)
-    CrossCor(filter, in=>out, activation)
-    CrossCor(filter, in => out, σ = identity; init = glorot_uniform,
-             stride = 1, pad = 0, dilation = 1)
+    CrossCor(size, in=>out)
+    CrossCor(size, in=>out, relu)
 
-Standard cross convolutional layer. `filter` should be a tuple like `(2, 2)`.
+Standard cross convolutional layer. `size` 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).
+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).
 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.
-
-# 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
@@ -415,39 +234,18 @@ struct CrossCor{N,M,F,A,V}
   dilation::NTuple{N,Int}
 end
 
-"""
-    CrossCor(weight::AbstractArray, bias::AbstractArray)
-    CrossCor(weight::AbstractArray, bias::AbstractArray, activation)
-
-Constructs the standard cross convolutional layer with user defined weight and bias
-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}
+function CrossCor(w::AbstractArray{T,N}, b::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
 
-function CrossCor(;weight::AbstractArray{T,N}, bias::Union{Zeros, AbstractVector{T}},
-                      activation = identity, stride = 1, pad = 0, dilation = 1) where {T,N}
-  CrossCor(weight, bias, activation, stride = stride, pad = pad, dilation = dilation)
-end
-
-function CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
-                  init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
-                  weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N
-
-  CrossCor(weight, bias, σ,
+CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
+     init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N =
+  CrossCor(init(k..., ch...), zeros(ch[2]), σ,
        stride = stride, pad = pad, dilation = dilation)
-end
 
 @functor CrossCor
 
@@ -477,66 +275,12 @@ end
 (a::CrossCor{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
   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))
-
 """
-    GlobalMaxPool()
+    MaxPool(k)
 
-Global max pooling layer.
+Max pooling layer. `k` stands for the size of the window for each dimension of the input.
 
-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.
-=======
+Takes the keyword arguments `pad` and `stride`.
 """
 struct MaxPool{N,M}
   k::NTuple{N,Int}
@@ -546,7 +290,8 @@ end
 
 function MaxPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N
   stride = expand(Val(N), stride)
-  pad = calc_padding(pad, k, 1, stride)
+  pad = expand(Val(2*N), pad)
+
   return MaxPool(k, pad, stride)
 end
 
@@ -559,14 +304,12 @@ function Base.show(io::IO, m::MaxPool)
   print(io, "MaxPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
 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; pad = 0, stride = k)
+    MeanPool(k)
 
-Mean pooling layer. `k` is the size of the window for each dimension of the input.
+Mean pooling layer. `k` stands for the size of the window for each dimension of the input.
 
-Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride.
+Takes the keyword arguments `pad` and `stride`.
 """
 struct MeanPool{N,M}
     k::NTuple{N,Int}
@@ -576,7 +319,7 @@ end
 
 function MeanPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N
   stride = expand(Val(N), stride)
-  pad = calc_padding(pad, k, 1, stride)
+  pad = expand(Val(2*N), pad)
   return MeanPool(k, pad, stride)
 end
 
@@ -588,5 +331,3 @@ end
 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))

src/layers/normalise.jl

@@ -2,23 +2,11 @@ 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 = :)
@@ -30,31 +18,22 @@ end
 """
     Dropout(p, dims = :)
 
-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`.
+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).
 """
 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, nothing)
+  Dropout{typeof(p),typeof(dims)}(p, dims)
 end
 
-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)
+(a::Dropout)(x) = dropout(x, a.p; dims = a.dims)
 
 function Base.show(io::IO, d::Dropout)
   print(io, "Dropout(", d.p)
@@ -64,25 +43,20 @@ end
 
 """
     AlphaDropout(p)
-
-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.
+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.
 """
 mutable struct AlphaDropout{F}
   p::F
-  active::Union{Bool, Nothing}
-  function AlphaDropout(p, active = nothing)
+  function AlphaDropout(p)
     @assert 0 ≤ p ≤ 1
-    new{typeof(p)}(p, active)
+    new{typeof(p)}(p)
   end
 end
 
 function (a::AlphaDropout)(x)
-  _isactive(a) || return x
+  istraining() || return x
   λ = eltype(x)(1.0507009873554804934193349852946)
   α = eltype(x)(1.6732632423543772848170429916717)
   α1 = eltype(x)(-λ*α)
@@ -94,15 +68,12 @@ 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 and standard
-deviation of each input before applying a per-neuron gain/bias.
+used with recurrent hidden states of size `h`. Normalises the mean/stddev of
+each input before applying a per-neuron gain/bias.
 """
 struct LayerNorm{T}
   diag::Diagonal{T}
@@ -124,8 +95,8 @@ end
               initβ = zeros, initγ = ones,
               ϵ = 1e-8, momentum = .1)
 
-[Batch Normalization](https://arxiv.org/pdf/1502.03167.pdf) layer.
-`channels` should be the size of the channel dimension in your data (see below).
+Batch 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
@@ -135,9 +106,10 @@ 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).
 
-Use [`testmode!`](@ref) during inference.
+See [Batch Normalization: Accelerating Deep Network Training by Reducing
+Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf).
 
-# Examples
+Example:
 ```julia
 m = Chain(
   Dense(28^2, 64),
@@ -155,16 +127,12 @@ 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, nothing)
+            zeros(chs), ones(chs), ϵ, momentum)
 
 trainable(bn::BatchNorm) = (bn.β, bn.γ)
 
@@ -177,7 +145,7 @@ function (BN::BatchNorm)(x)
   m = div(prod(size(x)), channels)
   γ = reshape(BN.γ, affine_shape...)
   β = reshape(BN.β, affine_shape...)
-  if !_isactive(BN)
+  if !istraining()
     μ = reshape(BN.μ, affine_shape...)
     σ² = reshape(BN.σ², affine_shape...)
     ϵ = BN.ϵ
@@ -202,15 +170,41 @@ 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}
@@ -221,44 +215,12 @@ 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, nothing)
+            zeros(chs), ones(chs), ϵ, momentum)
 
 trainable(in::InstanceNorm) = (in.β, in.γ)
 
@@ -275,7 +237,7 @@ 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 !_isactive(in)
+  if !istraining()
     μ = expand_inst(in.μ, affine_shape)
     σ² = expand_inst(in.σ², affine_shape)
     ϵ = in.ϵ
@@ -301,9 +263,6 @@ 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)")
@@ -311,27 +270,26 @@ function Base.show(io::IO, l::InstanceNorm)
 end
 
 """
-    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.
+This layer can outperform Batch-Normalization and Instance-Normalization.
 
-[Group Normalization](https://arxiv.org/pdf/1803.08494.pdf) layer.
-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)
 
-`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 are 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.
 
-Use [`testmode!`](@ref) during inference.
-
-# 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
+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
+```
+
+Link : https://arxiv.org/pdf/1803.08494.pdf
 """
 mutable struct GroupNorm{F,V,W,N,T}
   G::T # number of groups
@@ -342,16 +300,12 @@ 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, nothing)
+            zeros(G,1), ones(G,1), ϵ, momentum)
 
 trainable(gn::GroupNorm) = (gn.β, gn.γ)
 
@@ -375,7 +329,7 @@ function(gn::GroupNorm)(x)
   β = reshape(gn.β, affine_shape...)
 
   y = reshape(x,((size(x))[1:end-2]...,channels_per_group,groups,batches))
-  if !_isactive(gn)
+  if !istraining()
     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)
@@ -406,9 +360,6 @@ 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)")

src/layers/recurrent.jl

@@ -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}
@@ -45,12 +45,12 @@ Base.show(io::IO, m::Recur) = print(io, "Recur(", m.cell, ")")
 """
     reset!(rnn)
 
-Reset the hidden state of a recurrent layer back to its original value.
+Reset the hidden state of a recurrent layer back to its original value. See also
+`truncate!`.
 
-Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to:
-```julia
-rnn.state = hidden(rnn.cell)
-```
+Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to
+
+    rnn.state = hidden(rnn.cell)
 """
 reset!(m::Recur) = (m.state = m.init)
 reset!(m) = foreach(reset!, functor(m)[1])
@@ -136,8 +136,8 @@ Base.show(io::IO, l::LSTMCell) =
 """
     LSTM(in::Integer, out::Integer)
 
-[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.
+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.
@@ -177,8 +177,8 @@ Base.show(io::IO, l::GRUCell) =
 """
     GRU(in::Integer, out::Integer)
 
-[Gated Recurrent Unit](https://arxiv.org/abs/1406.1078) layer. Behaves like an
-RNN but generally exhibits a longer memory span over sequences.
+Gated Recurrent Unit 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.

src/layers/stateless.jl

@@ -1,296 +1,86 @@
+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
-"""
-#TODO: remove dropgrad when Zygote can handle this function with CuArrays
-function huber_loss(ŷ, y;  δ=eltype(ŷ)(1))
-   abs_error = abs.(ŷ .- y)
-   temp = Zygote.dropgrad(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(xlogy.(y, ŷ)) * 1 // size(y, 2)
+  return -sum(y .* log.(ŷ)) * 1 // size(y, 2)
 end
 
 function _crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, weight::Number)
-  return -sum(xlogy.(y, ŷ)) .* weight * 1 // size(y, 2)
+  return -sum(y .* log.(ŷ)) .* weight * 1 // size(y, 2)
 end
 
 function _crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, weight::AbstractVector)
-  return -sum(xlogy.(y, ŷ) .* weight) * 1 // size(y, 2)
+  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)
 
-"""
-    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(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)
+function logitcrossentropy(logŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight = 1)
+  return -sum(y .* logsoftmax(logŷ) .* 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.
 
-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
-```
+    julia> binarycrossentropy.(σ.([-1.1491, 0.8619, 0.3127]), [1, 1, 0.])
+    3-element Array{Float64,1}:
+    1.4244
+    0.352317
+    0.86167
 """
-binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -xlogy(y, ŷ + ϵ) - xlogy(1 - y, 1 - ŷ + ϵ)
+binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -y*log(ŷ + ϵ) - (1 - y)*log(1 - ŷ + ϵ)
 
 # Re-definition to fix interaction with CuArrays.
 CuArrays.@cufunc binarycrossentropy(ŷ, y; ϵ=eps(ŷ)) = -y*log(ŷ + ϵ) - (1 - y)*log(1 - ŷ + ϵ)
 
 """
-    logitbinarycrossentropy(ŷ, y)
+    logitbinarycrossentropy(logŷ, y)
 
-`logitbinarycrossentropy(ŷ, y)` is mathematically equivalent to
-[`Flux.binarycrossentropy(σ(ŷ), y)`](@ref) but it is more numerically stable.
+`logitbinarycrossentropy(logŷ, y)` is mathematically equivalent to `binarycrossentropy(σ(logŷ), y)`
+but it is more numerically stable.
 
-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
-```
+    julia> logitbinarycrossentropy.([-1.1491, 0.8619, 0.3127], [1, 1, 0.])
+    3-element Array{Float64,1}:
+     1.4244
+     0.352317
+     0.86167
 """
-logitbinarycrossentropy(ŷ, y) = (1 - y)*ŷ - logσ(ŷ)
+logitbinarycrossentropy(logŷ, y) = (1 - y)*logŷ - logσ(logŷ)
 
 # Re-definition to fix interaction with CuArrays.
-CuArrays.@cufunc logitbinarycrossentropy(ŷ, y) = (1 - y)*ŷ - logσ(ŷ)
+CuArrays.@cufunc logitbinarycrossentropy(logŷ, y) = (1 - y)*logŷ - logσ(logŷ)
 
 """
-    normalise(x; dims=1)
+    normalise(x::AbstractArray; dims=1)
 
-Normalise `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.
 
-```jldoctest
-julia> a = reshape(collect(1:9), 3, 3)
-3×3 Array{Int64,2}:
- 1  4  7
- 2  5  8
- 3  6  9
+    julia> a = reshape(collect(1:9), 3, 3)
+    3×3 Array{Int64,2}:
+     1  4  7
+     2  5  8
+     3  6  9
 
-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)
+    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, 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> 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)
   σ′ = std(x, dims = dims, mean = μ′, corrected=false)
   return (x .- μ′) ./ σ′
 end
-
-"""
-    kldivergence(ŷ, y)
-
-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(xlogx.(y)) * 1 //size(y,2)
-  cross_entropy = crossentropy(ŷ, y)
-  return entropy + cross_entropy
-end
-
-"""
-    poisson(ŷ, y)
-
-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 .- xlogy.(y, ŷ)) * 1 // size(y,2)
-
-"""
-    hinge(ŷ, y)
-
-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)
-
-"""
-    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
-
-"""
-    xlogx(x)
-Return `x * log(x)` for `x ≥ 0`, handling `x = 0` by taking the downward limit.
-"""
-function xlogx(x)
-  result = x * log(x)
-  ifelse(iszero(x), zero(result), result)
-end
-CuArrays.@cufunc function xlogx(x)
-  result = x * log(x)
-  ifelse(iszero(x), zero(result), result)
-end
-
-"""
-    xlogy(x, y)
-Return `x * log(y)` for `y > 0` with correct limit at `x = 0`.
-"""
-function xlogy(x, y)
-  result = x * log(y)
-  ifelse(iszero(x), zero(result), result)
-end
-CuArrays.@cufunc function xlogy(x, y)
-  result = x * log(y)
-  ifelse(iszero(x), zero(result), result)
-end
-
-@adjoint function broadcasted(::typeof(xlogy), x::Zygote.Numeric, y::Zygote.Numeric)
-  res = xlogy.(x, y)
-  res, Δ -> (nothing, Zygote.unbroadcast(x, xlogy.(Δ, y)), Zygote.unbroadcast(y, Δ .* x ./ y))
-end

src/onehot.jl

@@ -27,8 +27,7 @@ Base.getindex(xs::OneHotMatrix, ::Colon, ::Colon) = OneHotMatrix(xs.height, copy
 
 Base.getindex(xs::OneHotMatrix, i::Integer, ::Colon) = map(x -> x[i], xs.data)
 
-# remove workaround when https://github.com/JuliaGPU/CuArrays.jl/issues/676 is fixed
-A::AbstractMatrix * B::OneHotMatrix = A[:, cpu(map(x->x.ix, B.data))]
+A::AbstractMatrix * B::OneHotMatrix = A[:, map(x->x.ix, B.data)]
 
 Base.hcat(x::OneHotVector, xs::OneHotVector...) = OneHotMatrix(length(x), [x, xs...])
 
@@ -38,28 +37,30 @@ import Adapt: adapt, adapt_structure
 
 adapt_structure(T, xs::OneHotMatrix) = OneHotMatrix(xs.height, adapt(T, xs.data))
 
-import .CuArrays: CuArray, CuArrayStyle, cudaconvert
+import .CuArrays: CuArray, cudaconvert
 import Base.Broadcast: BroadcastStyle, ArrayStyle
-BroadcastStyle(::Type{<:OneHotMatrix{<:CuArray}}) = CuArrayStyle{2}()
+BroadcastStyle(::Type{<:OneHotMatrix{<:CuArray}}) = ArrayStyle{CuArray}()
 cudaconvert(x::OneHotMatrix{<:CuArray}) = OneHotMatrix(x.height, cudaconvert(x.data))
 
 """
     onehot(l, labels[, unk])
 
-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 raise an error.
+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
 
-# Examples
 ```jldoctest
-julia> Flux.onehot(:b, [:a, :b, :c])
+julia> using Flux: onehot
+
+julia> onehot(:b, [:a, :b, :c])
 3-element Flux.OneHotVector:
  0
  1
  0
 
-julia> Flux.onehot(:c, [:a, :b, :c])
+julia> onehot(:c, [:a, :b, :c])
 3-element Flux.OneHotVector:
  0
  0
@@ -81,14 +82,15 @@ end
 """
     onehotbatch(ls, labels[, unk...])
 
-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.
+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
 
-# Examples
 ```jldoctest
-julia> Flux.onehotbatch([:b, :a, :b], [:a, :b, :c])
+julia> using Flux: onehotbatch
+
+julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
 3×3 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:
  0  1  0
  1  0  1
@@ -105,12 +107,13 @@ Base.argmax(xs::OneHotVector) = xs.ix
 
 Inverse operations of [`onehot`](@ref).
 
-# Examples
 ```jldoctest
-julia> Flux.onecold([true, false, false], [:a, :b, :c])
+julia> using Flux: onecold
+
+julia> onecold([true, false, false], [:a, :b, :c])
 :a
 
-julia> Flux.onecold([0.3, 0.2, 0.5], [:a, :b, :c])
+julia> onecold([0.3, 0.2, 0.5], [:a, :b, :c])
 :c
 ```
 """
@@ -122,4 +125,6 @@ onecold(y::AbstractMatrix, labels...) =
 onecold(y::OneHotMatrix, labels...) =
   mapreduce(x -> Flux.onecold(x, labels...), |, y.data, dims = 2, init = 0)
 
-@nograd onecold, onehot, onehotbatch
+# TODO probably still want this as a custom adjoint Zygote
+# onecold(x::TrackedVector, l...) = onecold(data(x), l...)
+# onecold(x::TrackedMatrix, l...) = onecold(data(x), l...)

src/optimise/Optimise.jl

@@ -1,12 +1,9 @@
 module Optimise
 
-using LinearAlgebra
-
-export train!, update!,
-	Descent, ADAM, Momentum, Nesterov, RMSProp,
+export train!,
+	SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
 	ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM, ADAMW,RADAM, 
-	InvDecay, ExpDecay, WeightDecay, stop, Optimiser,
-	ClipValue, ClipNorm
+	InvDecay, ExpDecay, WeightDecay, stop, Optimiser
 
 include("optimisers.jl")
 include("train.jl")

src/optimise/optimisers.jl

@@ -1,4 +1,5 @@
 using Flux
+using Base: @get!
 using MacroTools: @forward
 
 const ϵ = 1e-8
@@ -6,25 +7,24 @@ const ϵ = 1e-8
 # TODO: should use weak refs
 
 """
-    Descent(η = 0.1)
+  Descent(η)
 
 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`): Amount by which gradients are discounted before updating
-                       the weights.
+## Parameters
+  - Learning Rate (η): The amount by which the gradients are discounted before updating the weights. Defaults to `0.1`.
 
-# Examples
-```julia
-opt = Descent()
+## Example
+```julia-repl
+opt = Descent() # uses default η (0.1)
 
-opt = Descent(0.3)
+opt = Descent(0.3) # use provided η
 
 ps = params(model)
 
 gs = gradient(ps) do
-    loss(x, y)
+  loss(x, y)
 end
 
 Flux.Optimise.update!(opt, ps, gs)
@@ -41,19 +41,17 @@ function apply!(o::Descent, x, Δ)
 end
 
 """
-    Momentum(η = 0.01, ρ = 0.9)
+    Momentum(η, ρ)
 
-Gradient descent optimizer with learning rate `η` and momentum `ρ`.
+Gradient descent with learning rate `η` and momentum `ρ`.
 
-# 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.
+## 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`.
 
-# Examples
+## Examples
 ```julia
-opt = Momentum()
+opt = Momentum() # uses defaults of η = 0.01 and ρ = 0.9
 
 opt = Momentum(0.01, 0.99)
 ```
@@ -74,19 +72,17 @@ function apply!(o::Momentum, x, Δ)
 end
 
 """
-    Nesterov(η = 0.001, ρ = 0.9)
+    Nesterov(η, ρ)
 
-Gradient descent optimizer with learning rate `η` and Nesterov momentum `ρ`.
+Gradient descent with learning rate  `η` and Nesterov momentum `ρ`.
 
-# 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.
+## 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`.
 
-# Examples
+## Examples
 ```julia
-opt = Nesterov()
+opt = Nesterov() # uses defaults η = 0.001 and ρ = 0.9
 
 opt = Nesterov(0.003, 0.95)
 ```
@@ -108,25 +104,23 @@ function apply!(o::Nesterov, x, Δ)
 end
 
 """
-    RMSProp(η = 0.001, ρ = 0.9)
+    RMSProp(η, ρ)
 
-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.
+Implements the RMSProp algortihm. Often a good choice for recurrent networks. Paramters other than learning rate generally don't need tuning.
 
-# 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.
+## Parameters
+  - Learning Rate (η): Defaults to `0.001`.
+  - Rho (ρ): Defaults to `0.9`.
 
-# Examples
+## Examples
 ```julia
-opt = RMSProp()
+opt = RMSProp() # uses default η = 0.001 and ρ = 0.9
 
 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
@@ -144,22 +138,23 @@ function apply!(o::RMSProp, x, Δ)
 end
 
 """
-    ADAM(η = 0.001, β::Tuple = (0.9, 0.999))
+    ADAM(η, β::Tuple)
 
-[ADAM](https://arxiv.org/abs/1412.6980v8) optimiser.
+Implements the ADAM optimiser.
 
-# 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.
+## 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
 
-# Examples
 ```julia
-opt = ADAM()
+opt = ADAM() # uses the default η = 0.001 and β = (0.9, 0.999)
 
 opt = ADAM(0.001, (0.9, 0.8))
 ```
+## References
+[ADAM](https://arxiv.org/abs/1412.6980v8) optimiser.
 """
 mutable struct ADAM
   eta::Float64
@@ -180,22 +175,24 @@ function apply!(o::ADAM, x, Δ)
 end
 
 """
-    RADAM(η = 0.001, β::Tuple = (0.9, 0.999))
+    RADAM(η, β::Tuple)
 
-[Rectified ADAM](https://arxiv.org/pdf/1908.03265v1.pdf) optimizer.
+Implements the rectified ADAM optimizer.
 
-# 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.
+## 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
 
-# Examples
 ```julia
-opt = RADAM()
+opt = RADAM() # uses the default η = 0.001 and β = (0.9, 0.999)
 
 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
@@ -223,22 +220,22 @@ function apply!(o::RADAM, x, Δ)
 end
 
 """
-    AdaMax(η = 0.001, β::Tuple = (0.9, 0.999))
+    AdaMax(η, β::Tuple)
 
-[AdaMax](https://arxiv.org/abs/1412.6980v9) is a variant of ADAM based on the ∞-norm.
+Variant of ADAM based on ∞-norm.
 
-# 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.
+## 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
+## Examples
 ```julia
-opt = AdaMax()
+opt = AdaMax() # uses default η and β
 
 opt = AdaMax(0.001, (0.9, 0.995))
 ```
+## References
+[AdaMax](https://arxiv.org/abs/1412.6980v9) optimiser.
 """
 mutable struct AdaMax
   eta::Float64
@@ -259,22 +256,23 @@ function apply!(o::AdaMax, x, Δ)
 end
 
 """
-    ADAGrad(η = 0.1)
+    ADAGrad(η)
 
-[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.
+Implements AdaGrad. It has parameter specific learning rates based on how frequently it is updated.
 
-# Parameters
-- Learning rate (`η`): Amount by which gradients are discounted before updating
-                       the weights.
+## Parameters
+  - Learning Rate (η): Defaults to `0.1`
 
-# Examples
+## Examples
 ```julia
-opt = ADAGrad()
+opt = ADAGrad() # uses default η = 0.1
 
 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
@@ -291,21 +289,21 @@ function apply!(o::ADAGrad, x, Δ)
 end
 
 """
-    ADADelta(ρ = 0.9)
+    ADADelta(ρ)
 
-[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.
+Version of ADAGrad that adapts learning rate based on a window of past gradient updates. Parameters don't need tuning.
 
-# Parameters
-- Rho (`ρ`): Factor by which the gradient is decayed at each time step.
+## Parameters
+  - Rho (ρ): Factor by which gradient is decayed at each time step. Defaults to `0.9`.
 
-# Examples
+## Examples
 ```julia
-opt = ADADelta()
-
+opt = ADADelta() # uses default ρ = 0.9
 opt = ADADelta(0.89)
 ```
+
+## References
+[ADADelta](https://arxiv.org/abs/1212.5701) optimiser.
 """
 mutable struct ADADelta
   rho::Float64
@@ -324,23 +322,22 @@ function apply!(o::ADADelta, x, Δ)
 end
 
 """
-    AMSGrad(η = 0.001, β::Tuple = (0.9, 0.999))
+    AMSGrad(η, β::Tuple)
 
-The [AMSGrad](https://openreview.net/forum?id=ryQu7f-RZ) version of the ADAM
-optimiser. Parameters don't need tuning.
+Implements AMSGrad version of the ADAM optimiser. Parameters don't need tuning.
 
-# 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.
+## 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
+## Examples
 ```julia
-opt = AMSGrad()
-
+opt = AMSGrad() # uses default η and β
 opt = AMSGrad(0.001, (0.89, 0.995))
 ```
+
+## References
+[AMSGrad](https://openreview.net/forum?id=ryQu7f-RZ) optimiser.
 """
 mutable struct AMSGrad
   eta::Float64
@@ -360,23 +357,22 @@ function apply!(o::AMSGrad, x, Δ)
 end
 
 """
-    NADAM(η = 0.001, β::Tuple = (0.9, 0.999))
+    NADAM(η, β::Tuple)
 
-[NADAM](http://cs229.stanford.edu/proj2015/054_report.pdf) is a Nesterov variant of ADAM.
-Parameters don't need tuning.
+Nesterov variant of ADAM. Parameters don't need tuning.
 
-# 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.
+## 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
+## Examples
 ```julia
-opt = NADAM()
-
+opt = NADAM() # uses default η and β
 opt = NADAM(0.002, (0.89, 0.995))
 ```
+
+## References
+[NADAM](http://cs229.stanford.edu/proj2015/054_report.pdf) optimiser.
 """
 mutable struct NADAM
   eta::Float64
@@ -397,24 +393,23 @@ function apply!(o::NADAM, x, Δ)
 end
 
 """
-    ADAMW(η = 0.001, β::Tuple = (0.9, 0.999), decay = 0)
+    ADAMW(η, β::Tuple, decay)
 
-[ADAMW](https://arxiv.org/abs/1711.05101) is a variant of ADAM fixing (as in repairing) its
-weight decay regularization.
+Variant of ADAM defined by fixing weight decay regularization.
 
-# 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.
+## 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.
 
-# Examples
+## Examples
 ```julia
-opt = ADAMW()
-
+opt = ADAMW() # uses default η, β and decay
 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))
@@ -447,15 +442,16 @@ function apply!(o::Optimiser, x, Δ)
 end
 
 """
-    InvDecay(γ = 0.001)
+  InvDecay(γ)
 
-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.
+Applies inverse time decay to an optimiser
 
-# Examples
+## Parameters
+  - gamma (γ): Defaults to `0.001`
+
+## Example
 ```julia
-Optimiser(InvDecay(..), Opt(..))
+  Optimiser(InvDecay(..), Opt(..))
 ```
 """
 mutable struct InvDecay
@@ -474,25 +470,22 @@ function apply!(o::InvDecay, x, Δ)
 end
 
 """
-    ExpDecay(η = 0.001, decay = 0.1, decay_step = 1000, clip = 1e-4)
+  ExpDecay(eta, decay, decay_step, clip)
 
-Discount the learning rate `η` by the factor `decay` every `decay_step` steps till
-a minimum of `clip`.
+Discount the learning rate `eta` by `decay` every `decay_step` till a minimum of `clip`.
 
-# 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.
+## 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`.
 
-# Examples
+## Example
 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
@@ -509,19 +502,19 @@ function apply!(o::ExpDecay, x, Δ)
   η, s, decay = o.eta, o.step, o.decay
   n = o.current[x] = get(o.current, x, 0) + 1
   if o.current[x]%s == 0 && count(x -> x%s == 0, values(o.current)) == 1
-    η = max(η * decay, o.clip)
+    η = max(η * decay^(s / n), o.clip)
     o.eta = η
   end
   @. Δ *= η
 end
 
 """
-    WeightDecay(wd = 0)
+  WeightDecay(wd)
 
-Decay weights by `wd`.
+Decays the weight by `wd`
 
-# Parameters
-- Weight decay (`wd`)
+## Parameters
+  - weight decay (wd): 0
 """
 mutable struct WeightDecay
   wd::Real
@@ -533,31 +526,3 @@ function apply!(o::WeightDecay, x, Δ)
   wd = o.wd
   @. Δ += wd * x
 end
-
-"""
-    ClipValue(thresh)
-
-Clip gradients when their absolute value exceeds `thresh`.
-"""
-mutable struct ClipValue{T}
-    thresh::T
-end
-
-apply!(o::ClipValue, x, Δ) = clamp!(Δ, -o.thresh, o.thresh)
-
-"""
-    ClipNorm(thresh)
-
-Clip gradients when their L2 norm exceeds `thresh`.
-"""
-mutable struct ClipNorm{T}
-    thresh::T
-end
-
-function apply!(o::ClipNorm, x, Δ)
-    Δnrm = norm(Δ)
-    if Δnrm > o.thresh
-        rmul!(Δ, o.thresh / Δnrm)
-    end
-    return Δ
-end

src/optimise/train.jl

@@ -1,26 +1,11 @@
 using Juno
 import Zygote: Params, gradient
 
-
-
-"""
-    update!(x, x̄)
-
-Update the array `x` according to `x .-= x̄`.
-"""
 function update!(x::AbstractArray, x̄)
-  x .-= x̄
+  x .+= x̄
+  return x
 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
@@ -43,10 +28,11 @@ struct StopException <: Exception end
     stop()
 
 Call `Flux.stop()` in a callback to indicate when a callback condition is met.
-This will trigger the train loop to stop and exit.
+This would trigger the train loop to stop and exit.
 
-# Examples
 ```julia
+# Example callback:
+
 cb = function ()
   accuracy() > 0.9 && Flux.stop()
 end
@@ -59,18 +45,18 @@ end
 """
     train!(loss, params, data, opt; cb)
 
-For each datapoint `d` in `data` compute the gradient of `loss(d...)` through
-backpropagation and call the optimizer `opt`.
+For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
+backpropagation and calls the optimizer `opt`.
 
-In case datapoints `d` are of numeric array type, assume no splatting is needed
-and compute the gradient of `loss(d)`.
+Takes a callback as keyword argument `cb`. For example, this will print "training"
+every 10 seconds:
 
-A callback is given with the keyword argument `cb`. For example, this will print
-"training" every 10 seconds (using [`Flux.throttle`](@ref)):
+```julia
+Flux.train!(loss, params, data, opt,
+            cb = throttle(() -> println("training"), 10))
+```
 
-    train!(loss, params, data, opt, cb = throttle(() -> println("training"), 10))
-
-The callback can call [`Flux.stop`](@ref) to interrupt the training loop.
+The callback can call `Flux.stop()` to interrupt the training loop.
 
 Multiple optimisers and callbacks can be passed to `opt` and `cb` as arrays.
 """
@@ -79,14 +65,8 @@ function train!(loss, ps, data, opt; cb = () -> ())
   cb = runall(cb)
   @progress for d in data
     try
-      if d isa AbstractArray{<:Number}
-        gs = gradient(ps) do
-          loss(d)
-        end
-      else
-        gs = gradient(ps) do
-          loss(d...)
-        end
+      gs = gradient(ps) do
+        loss(d...)
       end
       update!(opt, ps, gs)
       cb()
@@ -106,12 +86,11 @@ end
 Run `body` `N` times. Mainly useful for quickly doing multiple epochs of
 training in a REPL.
 
-# Examples
-```jldoctest
-julia> Flux.@epochs 2 println("hello")
-[ Info: Epoch 1
+```julia
+julia> @epochs 2 println("hello")
+INFO: Epoch 1
 hello
-[ Info: Epoch 2
+INFO: Epoch 2
 hello
 ```
 """

src/utils.jl

@@ -1,40 +1,10 @@
 # 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...)
@@ -43,81 +13,9 @@ 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)]
 
 """
@@ -125,16 +23,9 @@ unstack(xs, dim) = [copy(selectdim(xs, dim, i)) for i in 1:size(xs, dim)]
 
 Split `xs` into `n` parts.
 
-# 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}:
+```julia
+julia> chunk(1:10, 3)
+3-element Array{Array{Int64,1},1}:
  [1, 2, 3, 4]
  [5, 6, 7, 8]
  [9, 10]
@@ -149,12 +40,11 @@ batchindex(xs, i) = (reverse(Base.tail(reverse(axes(xs))))..., i)
 
 Count the number of times that each element of `xs` appears.
 
-# Examples
-```jldoctest
-julia> Flux.frequencies(['a','b','b'])
+```julia
+julia> frequencies(['a','b','b'])
 Dict{Char,Int64} with 2 entries:
-  'a' => 1
   'b' => 2
+  'a' => 1
 ```
 """
 function frequencies(xs)
@@ -170,13 +60,12 @@ 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.
 
-# Examples
-```jldoctest
-julia> Flux.batch([[1,2,3],[4,5,6]])
+```julia
+julia> batch([[1,2,3],[4,5,6]])
 3×2 Array{Int64,2}:
  1  4
  2  5
@@ -193,25 +82,6 @@ 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))]
 
 """
@@ -220,9 +90,8 @@ 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`.
 
-# Examples
-```jldoctest
-julia> Flux.batchseq([[1, 2, 3], [4, 5]], 0)
+```julia
+julia> batchseq([[1, 2, 3], [4, 5]], 0)
 3-element Array{Array{Int64,1},1}:
  [1, 4]
  [2, 5]
@@ -234,64 +103,14 @@ function batchseq(xs, pad = nothing, n = maximum(length(x) for x in xs))
   [batch([xs_[j][i] for j = 1:length(xs_)]) for i = 1:n]
 end
 
-# Flattening models to weight vectors, and back
-
-function _restructure(m, xs)
-  i = 0
-  fmap(m) do x
-    x isa AbstractArray || return x
-    x = reshape(xs[i.+(1:length(x))], size(x))
-    i += length(x)
-    return x
-  end
-end
-
-@adjoint function _restructure(m, xs)
-  _restructure(m, xs), dm -> (nothing,destructure(dm)[1])
-end
-
-"""
-    destructure(m)
-
-Flatten a model's parameters into a single weight vector.
-
-    julia> m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
-    Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
-
-    julia> θ, re = destructure(m);
-
-    julia> θ
-    67-element Array{Float32,1}:
-    -0.1407104
-    ...
-
-The second return value `re` allows you to reconstruct the original network after making
-modifications to the weight vector (for example, with a hypernetwork).
-
-    julia> re(θ .* 2)
-    Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
-"""
-function destructure(m)
-  xs = Zygote.Buffer([])
-  fmap(m) do x
-    x isa AbstractArray && push!(xs, x)
-    return x
-  end
-  return vcat(vec.(copy(xs))...), p -> _restructure(m, p)
-end
-
 # Other
 
 """
-    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`.
+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.
 """
 function throttle(f, timeout; leading=true, trailing=false)
   cooldown = true

src/zeros.jl

@@ -1,106 +0,0 @@
-import Base: +, -, *, reshape, size
-import Base.Broadcast: broadcasted, Broadcasted, BroadcastStyle
-
-"""
-    Zeros()
-    Zeros(size...)
-    Zeros(Type, size...)
-
-Acts as a stand-in for an array of zeros that can be
-used during training which is ignored by the optimisers.
-
-Useful to turn bias off for a forward pass of a layer.
-
-## Examples
-
-```julia
-julia> Flux.Zeros(3,3)
-3×3 Flux.Zeros{Bool,2}:
- false  false  false
- false  false  false
- false  false  false
-
-julia> Flux.Zeros(Float32, 3,3)
-3×3 Flux.Zeros{Float32,2}:
- 0.0  0.0  0.0
- 0.0  0.0  0.0
- 0.0  0.0  0.0
-
-julia> rand(3,3) .+ Flux.Zeros()
-3×3 Array{Float64,2}:
- 0.198739  0.490459  0.785386
- 0.779074  0.39986   0.66383
- 0.854981  0.447292  0.314497
-
-julia> bias_less_conv = Conv((2,2), 1=>3, bias = Flux.Zeros())
-Conv((2, 2), 1=>3)
-```
-"""
-struct Zeros{T,N} <: AbstractArray{T,N}
-  size::Tuple
-end
-
-Zeros(::Type{T}, sz...) where T = Zeros{T,length(sz)}(sz)
-Zeros(sz::Integer...) = Zeros(Bool, sz...)
-
-Base.size(xs::Zeros) = xs.size
-Base.axes(xs::Zeros) = Base.OneTo.(size(xs))
-
-Base.IndexStyle(::Type{<:Zeros}) = IndexLinear()
-
-Base.getindex(xs::Zeros{T,N}, I::Int) where {T,N} = zero(T)
-Base.getindex(xs::Zeros{T,N}, inds::Union{Base.OneTo, Base.UnitRange}) where {T,N} =
-              Zeros(T, length(inds))
-
-Base.collect(xs::Zeros{T,N}) where {T,N} = fill(zero(T), size(xs))
-
-@adjoint reshape(xs::Zeros{T}, dims...) where T =
-                reshape(xs, dims...), _ -> nothing
-
-# Define basic ops
-for f in (:+, :-)
-  @eval @inline function $f(a::Union{AbstractArray{<:Number}, Zeros}, b::Zeros)
-    @assert size(a) == size(b) throw(DimensionMismatch("dimensions must match"))
-    a
-  end
-end
-
-+(a::Zeros, b::AbstractArray) = b + a
--(a::Zeros, b::AbstractArray) = -b + a
-
-Base.copy(xs::Zeros{T,N}) where {T,N} = xs
-
-# Define broadcasting behaviour
-for op in (:+, :-)
-  @eval function broadcasted(::typeof($op), a::AbstractArray, b::Zeros)
-    bs = Broadcast.broadcast_shape(size(a), size(b))
-    size(a) == bs && return a
-    sz = similar(a, bs)
-    sz .= a
-  end
-end
-
-broadcasted(::typeof(+), a::Zeros, b::AbstractArray) = broadcasted(+, b, a)
-broadcasted(::typeof(-), a::Zeros, b::AbstractArray) = broadcasted(+, -b, a)
-
-function broadcasted(::typeof(*), a::AbstractArray, b::Zeros)
-  Zeros(Broadcast.broadcast_shape(size(a), size(b))...)
-end
-
-broadcasted(::typeof(*), a::Zeros, b::AbstractArray) = broadcasted(*, b, a)
-
-for op in (:+, :-, :*)
-  @eval broadcasted(::typeof($op), a::Zeros, b::Zeros) = Zeros(Broadcast.broadcast_shape(size(a), size(b))...)
-end
-
-# Some opportunities to avoid scalar indexing, intermediaries
-# Since it replicates a little of what we expect Base to do,
-# it should be possible to remove in the future, but for now,
-# these help with performance.
-broadcasted(::typeof(+), a::AbstractArray, b::Zeros{T,0}) where T = a
-broadcasted(::typeof(+), a::Zeros{T,0}, b::AbstractArray) where T = b
-broadcasted(::typeof(-), a::AbstractArray, b::Zeros{T,0}) where T = a
-broadcasted(::typeof(-), a::Zeros{T,0}, b::AbstractArray) where T = -b
-broadcasted(::typeof(*), a::AbstractArray, b::Zeros{T,0}) where T = zero(a)
-broadcasted(::typeof(*), a::Zeros{T,0}, b::AbstractArray) where T = zero(b)
-broadcasted(::typeof(/), a::Zeros{T,0}, b::AbstractArray) where T = zero(b)

test/cuda/cuda.jl

@@ -25,7 +25,7 @@ cm = gpu(m)
 @test all(p isa CuArray for p in params(cm))
 @test cm(gpu(rand(10, 10))) isa CuArray{Float32,2}
 
-x = [1.,2.,3.]
+x = [1,2,3]
 cx = gpu(x)
 @test Flux.crossentropy(x,x) ≈ Flux.crossentropy(cx,cx)
 @test Flux.crossentropy(x,x, weight=1.0) ≈ Flux.crossentropy(cx,cx, weight=1.0)
@@ -33,8 +33,8 @@ cx = gpu(x)
 
 x = [-1.1491, 0.8619, 0.3127]
 y = [1, 1, 0.]
-@test Flux.binarycrossentropy.(σ.(x),y) ≈ Array(Flux.binarycrossentropy.(cu(σ.(x)),cu(y)))
-@test Flux.logitbinarycrossentropy.(x,y) ≈ Array(Flux.logitbinarycrossentropy.(cu(x),cu(y)))
+@test Flux.binarycrossentropy.(σ.(x),y) ≈ Flux.binarycrossentropy.(cu(σ.(x)),cu(y))
+@test Flux.logitbinarycrossentropy.(x,y) ≈ Flux.logitbinarycrossentropy.(cu(x),cu(y))
 
 xs = rand(5, 5)
 ys = Flux.onehotbatch(1:5,1:5)
@@ -58,13 +58,6 @@ 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")

test/data.jl

@@ -1,116 +1,22 @@
-@testset "DataLoader" begin
-    X = reshape([1:10;], (2, 5))
-    Y = [1:5;]
+using Flux.Data
+using Test
 
-    d = DataLoader(X, batchsize=2)
-    @inferred first(d)
-    batches = collect(d)
-    @test eltype(batches) == eltype(d) == typeof(X)
-    @test length(batches) == 3
-    @test batches[1] == X[:,1:2]
-    @test batches[2] == X[:,3:4]
-    @test batches[3] == X[:,5:5]
+@test cmudict()["CATASTROPHE"] == :[K,AH0,T,AE1,S,T,R,AH0,F,IY0].args
 
-    d = DataLoader(X, batchsize=2, partial=false)
-    @inferred first(d)
-    batches = collect(d)
-    @test eltype(batches) == eltype(d) == typeof(X)
-    @test length(batches) == 2
-    @test batches[1] == X[:,1:2]
-    @test batches[2] == X[:,3:4]
+@test length(CMUDict.phones()) == 39
 
-    d = DataLoader((X,), batchsize=2, partial=false)
-    @inferred first(d)
-    batches = collect(d)
-    @test eltype(batches) == eltype(d) == Tuple{typeof(X)}
-    @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)
-    @inferred first(d)
-    batches = collect(d)
-    @test eltype(batches) == eltype(d) == Tuple{typeof(X), typeof(Y)}
-    @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 with NamedTuple
-    d = DataLoader((x=X, y=Y), batchsize=2)
-    @inferred first(d)
-    batches = collect(d)
-    @test eltype(batches) == eltype(d) == NamedTuple{(:x, :y), Tuple{typeof(X), typeof(Y)}}
-    @test length(batches) == 3
-    @test length(batches[1]) == 2
-    @test length(batches[2]) == 2
-    @test length(batches[3]) == 2
-    @test batches[1][1] == batches[1].x == X[:,1:2]
-    @test batches[1][2] == batches[1].y == Y[1:2]
-    @test batches[2][1] == batches[2].x == X[:,3:4]
-    @test batches[2][2] == batches[2].y == Y[3:4]
-    @test batches[3][1] == batches[3].x == X[:,5:5]
-    @test batches[3][2] == batches[3].y == Y[5:5]
+@test FashionMNIST.images()[1] isa Matrix
+@test FashionMNIST.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 Data.Sentiment.train() isa Vector{Data.Tree{Any}}
 
-    # 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 Iris.features() isa Matrix
+@test size(Iris.features()) == (4,150)
 
-@testset "CMUDict" begin 
-    @test cmudict()["CATASTROPHE"] == :[K,AH0,T,AE1,S,T,R,AH0,F,IY0].args
-
-    @test length(CMUDict.phones()) == 39
-
-    @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
+@test Iris.labels() isa Vector{String}
+@test size(Iris.labels()) == (150,)

test/layers/basic.jl

@@ -28,14 +28,6 @@ import Flux: activations
   end
 
   @testset "Dense" begin
-    @testset "constructors" begin
-      @test size(Dense(10, 100).W) == (100, 10)
-      @test Dense(rand(100,10), rand(10)).σ == identity
-
-      @test_throws MethodError Dense(10, 10.5)
-      @test_throws MethodError Dense(10, 10.5, tanh)
-    end
-
     @test  length(Dense(10, 5)(randn(10))) == 5
     @test_throws DimensionMismatch Dense(10, 5)(randn(1))
     @test_throws MethodError Dense(10, 5)(1) # avoid broadcasting
@@ -45,6 +37,7 @@ import Flux: activations
     @test Dense(10, 1, identity, initW = ones, initb = zeros)(ones(10,2)) == 10*ones(1, 2)
     @test Dense(10, 2, identity, initW = ones, initb = zeros)(ones(10,1)) == 10*ones(2, 1)
     @test Dense(10, 2, identity, initW = ones, initb = zeros)([ones(10,1) 2*ones(10,1)]) == [10 20; 10 20]
+
   end
 
   @testset "Diagonal" begin
@@ -99,19 +92,4 @@ import Flux: activations
       @test size(SkipConnection(Dense(10,10), (a,b) -> cat(a, b, dims = 2))(input)) == (10,4)
     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

test/layers/conv.jl

@@ -4,10 +4,6 @@ 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))
@@ -25,35 +21,6 @@ end
     Dense(288, 10), softmax)
 
   @test size(m(r)) == (10, 5)
-
-  # Test bias switch
-  bias = Conv(ones(Float32, 2, 2, 1, 3), ones(Float32, 3))
-  ip = zeros(Float32, 28,28,1,1)
-
-  op = bias(ip)
-  @test sum(op) == prod(size(op))
-
-  bias = Conv((2,2), 1=>3, bias = Flux.Zeros())
-  op = bias(ip)
-  @test sum(op) === 0.f0
-  gs = gradient(() -> sum(bias(ip)), Flux.params(bias))
-  @test gs[bias.bias] == nothing
-
-  # Train w/o bias and make sure no convergence happens
-  # when only bias can be converged
-  bias = Conv((2, 2), 1=>3, bias = Flux.Zeros());
-  ip = zeros(Float32, 28,28,1,1)
-  op = zeros(Float32, 27,27,3,1) .+ 2.f0
-  opt = Descent()
-
-  for _ = 1:10^3
-    gs = gradient(params(bias)) do
-      Flux.mse(bias(ip), op)
-    end
-    Flux.Optimise.update!(opt, params(bias), gs)
-  end
-
-  @test Flux.mse(bias(ip), op) ≈ 4.f0
 end
 
 @testset "asymmetric padding" begin
@@ -99,7 +66,7 @@ end
   w = rand(2,2,1,1)
   y = CrossCor(w, [0.0])
 
-  @test isapprox(sum(w .* x[1:2, 1:2, :, :]), y(x)[1, 1, 1, 1], rtol=1e-7)
+  @test sum(w .* x[1:2, 1:2, :, :]) == y(x)[1, 1, 1, 1]
 
   r = zeros(Float32, 28, 28, 1, 5)
   m = Chain(
@@ -122,17 +89,17 @@ end
   l = Conv((3,3), 1=>1)
   expected = zeros(eltype(l.weight),5,5,1,1)
   expected[2:end-1,2:end-1,1,1] = l.weight
-  @test expected ≈ l(data)
+  @test expected == l(data)
 
   l = Conv((3,1), 1=>1)
   expected = zeros(eltype(l.weight),5,7,1,1)
   expected[2:end-1,4,1,1] = l.weight
-  @test expected ≈ l(data)
+  @test expected == l(data)
 
   l = Conv((1,3), 1=>1)
   expected = zeros(eltype(l.weight),7,5,1,1)
   expected[4,2:end-1,1,1] = l.weight
-  @test expected ≈ l(data)
+  @test expected == l(data)
 
   @test begin
     # we test that the next expression does not throw
@@ -140,79 +107,3 @@ end
     true
   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
-
-@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

test/layers/normalisation.jl

@@ -1,32 +1,30 @@
 using Flux, Test, Statistics
 using Zygote: pullback
 
-evalwgrad(f, x...) = pullback(f, x...)[1]
+trainmode(f, x...) = pullback(f, x...)[1]
+trainmode(f) = (x...) -> trainmode(f, x...)
 
 @testset "Dropout" begin
   x = [1.,2.,3.]
   @test x == Dropout(0.1)(x)
-  @test x == evalwgrad(Dropout(0), x)
-  @test zero(x) == evalwgrad(Dropout(1), x)
+  @test x == trainmode(Dropout(0), x)
+  @test zero(x) == trainmode(Dropout(1), x)
 
   x = rand(100)
   m = Dropout(0.9)
-  y = evalwgrad(m, x)
+  y = trainmode(m, x)
   @test count(a->a==0, y) > 50
-  testmode!(m, true)
-  y = evalwgrad(m, x) # should override istraining
+  y = m(x)
   @test count(a->a==0, y) == 0
-  testmode!(m, false)
-  y = evalwgrad(m, x)
+  y = trainmode(m, x)
   @test count(a->a==0, y) > 50
 
   x = rand(Float32, 100)
   m = Chain(Dense(100,100),
             Dropout(0.9))
-  y = evalwgrad(m, x)
+  y = trainmode(m, x)
   @test count(a->a == 0, y) > 50
-  testmode!(m, true)
-  y = evalwgrad(m, x) # should override istraining
+  y = m(x)
   @test count(a->a == 0, y) == 0
 
   x = rand(100, 50)
@@ -51,7 +49,7 @@ end
     # initial m.σ is 1
     # initial m.μ is 0
 
-    y = evalwgrad(m, x)
+    y = trainmode(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}:
@@ -84,19 +82,19 @@ 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
@@ -119,7 +117,7 @@ end
       x = Float64.(x)
       @test m.β == [0, 0]  # initβ(2)
       @test m.γ == [1, 1]  # initγ(2)
-      y = evalwgrad(m, x)
+      y = trainmode(m, x)
 
       #julia> x
       #[:, :, 1] =
@@ -164,7 +162,7 @@ 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...)
@@ -174,14 +172,14 @@ 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 = evalwgrad(m, x)
+    y = trainmode(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
@@ -206,7 +204,7 @@ if VERSION >= v"1.1"
       @test m.β == [0, 0, 0, 0]  # initβ(32)
       @test m.γ == [1, 1, 1, 1]  # initγ(32)
 
-      y = evalwgrad(m, x)
+      y = trainmode(m, x)
 
       #julia> x
       #[:, :, 1]  =
@@ -265,7 +263,7 @@ 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...)
@@ -275,20 +273,20 @@ 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 = evalwgrad(m, x)
+    y = trainmode(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

test/layers/stateless.jl

@@ -1,26 +1,9 @@
 using Test
 using Flux: onehotbatch, mse, crossentropy, logitcrossentropy,
-            σ, binarycrossentropy, logitbinarycrossentropy, flatten,
-            xlogx, xlogy
+            σ, binarycrossentropy, logitbinarycrossentropy
 
 const ϵ = 1e-7
 
-@testset "xlogx & xlogy" begin
-  @test iszero(xlogx(0))
-  @test isnan(xlogx(NaN))
-  @test xlogx(2) ≈ 2.0 * log(2.0)
-  @inferred xlogx(2)
-  @inferred xlogx(0)
-
-  @test iszero(xlogy(0, 1))
-  @test isnan(xlogy(NaN, 1))
-  @test isnan(xlogy(1, NaN))
-  @test isnan(xlogy(NaN, NaN))
-  @test xlogy(2, 3) ≈ 2.0 * log(3.0)
-  @inferred xlogy(2, 3)
-  @inferred xlogy(0, 1)
-end
-
 @testset "losses" begin
   # First, regression-style y's
   y = [1, 1, 0, 0]
@@ -30,20 +13,6 @@ end
     @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)
   ŷ = [.1 .9; .9 .1; .9 .1; .1 .9]'
@@ -52,7 +21,6 @@ end
   lossvalue = 1.203972804325936
 
   @testset "crossentropy" begin
-    @test crossentropy([0.1,0.0,0.9], [0.1,0.0,0.9]) ≈ crossentropy([0.1,0.9], [0.1,0.9])
     @test crossentropy(ŷ, y) ≈ lossvalue
   end
 
@@ -82,52 +50,11 @@ end
     @test logitbinarycrossentropy.(logŷ, y) ≈ binarycrossentropy.(σ.(logŷ), y; ϵ=0)
   end
 
-  y = [1 2 3]
-  ŷ = [4.0 5.0 6.0]
-  @testset "kldivergence" begin
-    @test Flux.kldivergence([0.1,0.0,0.9], [0.1,0.0,0.9]) ≈ Flux.kldivergence([0.1,0.9], [0.1,0.9])
-    @test Flux.kldivergence(ŷ, y) ≈ -1.7661057888493457
-    @test Flux.kldivergence(y, y) ≈ 0
-  end
-
-  y = [1 2 3 4]
-  ŷ = [5.0 6.0 7.0 8.0]
-  @testset "hinge" begin
-    @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]
-  ŷ = [0.4 0.5 0.6]
-  @testset "poisson" begin
-    @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,
-              Flux.mae, Flux.huber_loss, Flux.msle, Flux.squared_hinge, Flux.dice_coeff_loss, Flux.tversky_loss)
+      for f in (mse, crossentropy, logitcrossentropy)
         fwd, back = Flux.pullback(f, ŷ, y)
         @test fwd isa T
         @test eltype(back(one(T))[1]) == T
@@ -135,10 +62,3 @@ end
     end
   end
 end
-
-@testset "helpers" begin
-  @testset "flatten" begin
-    x = randn(Float32, 10, 10, 3, 2)
-    @test size(flatten(x)) == (300, 2)
-  end
-end

test/optimise.jl

@@ -57,57 +57,35 @@ end
 end
 
 @testset "ExpDecay" begin
-
-  @testset "Sanity Check" begin
-    o = ExpDecay(0.2, 0.5, 1, 1e-3)
-    p = [0.0]
-    steps = 1:8
-    eta_expected = @. max(o.eta * 0.5 ^ steps, o.clip)
-    eta_actual = [Optimise.apply!(o, p, [1.0])[1] for _ in steps]
-    @test eta_actual == eta_expected
-  end
-
-  w = randn(10, 10)
-  o = ExpDecay(0.1, 0.1, 1000, 1e-4)
-  w1 = randn(10,10)
-  loss(x) = Flux.mse(w*x, w1*x)
-  flag = 1
-  decay_steps = []
-  for t = 1:10^5
-    prev_eta = o.eta
-    θ = Params([w1])
-    x = rand(10)
-    θ̄ = gradient(() -> loss(x), θ)
-    prev_grad = collect(θ̄[w1])
-    delta = Optimise.apply!(o, w1, θ̄[w1])
-    w1 .-= delta
-    new_eta = o.eta
-    if new_eta != prev_eta
-      push!(decay_steps, t)
-    end
-    array = fill(o.eta, size(prev_grad))
-    if array .* prev_grad != delta
-      flag = 0
-    end
-  end
-  @test flag == 1
-  # Test to check if decay happens at decay steps. Eta reaches clip value (1e-4) after 4000 steps (decay by 0.1 every 1000 steps starting at 0.1).
-  ground_truth = []
-  for i in 1:4
-    push!(ground_truth, 1000*i)  # Expected decay steps for this example.
-  end
-  @test decay_steps == ground_truth
-  @test o.eta == o.clip
-end
-
-@testset "Clipping" begin
     w = randn(10, 10)
-    loss(x) = sum(w * x)
-    θ = Params([w])
-    x = 1000 * randn(10)
-    w̄ = gradient(() -> loss(x), θ)[w]
-    w̄_value = Optimise.apply!(ClipValue(1.0), w, copy(w̄))
-    @test all(w̄_value .<= 1)
-    w̄_norm = Optimise.apply!(ClipNorm(1.0), w, copy(w̄))
-    @test norm(w̄_norm) <= 1
+    o = ExpDecay(0.1, 0.1, 1000, 1e-4)
+    w1 = randn(10,10)
+    loss(x) = Flux.mse(w*x, w1*x)
+    flag = 1
+    decay_steps = []
+    for t = 1:10^5
+      prev_eta = o.eta
+      θ = Params([w1])
+      x = rand(10)
+      θ̄ = gradient(() -> loss(x), θ)
+      prev_grad = collect(θ̄[w1])
+      delta = Optimise.apply!(o, w1, θ̄[w1])
+      w1 .-= delta
+      new_eta = o.eta
+      if new_eta != prev_eta
+        push!(decay_steps, t)
+      end
+      array = fill(o.eta, size(prev_grad))
+      if array .* prev_grad != delta
+        flag = 0
+      end
+    end
+    @test flag == 1
+    # Test to check if decay happens at decay steps. Eta reaches clip value eventually.
+    ground_truth = []
+    for i in 1:11
+      push!(ground_truth, 1000*i)  # Expected decay steps for this example.
+    end
+    @test decay_steps == ground_truth
+    @test o.eta == o.clip
 end

test/runtests.jl

@@ -1,46 +1,32 @@
-using Flux 
-using Flux.Data
-using Test 
-using Random, Statistics, LinearAlgebra
-using IterTools: ncycle
+using Flux, Test, Random, Statistics, Documenter
+using Random
 
 Random.seed!(0)
 
-@testset "Utils" begin
-  include("utils.jl")
+@testset "Flux" begin
+
+@info "Testing Basics"
+
+include("utils.jl")
+include("onehot.jl")
+include("optimise.jl")
+include("data.jl")
+
+@info "Testing Layers"
+
+include("layers/basic.jl")
+include("layers/normalisation.jl")
+include("layers/stateless.jl")
+include("layers/conv.jl")
+
+if Flux.use_cuda[]
+  include("cuda/cuda.jl")
+else
+  @warn "CUDA unavailable, not testing GPU support"
 end
 
-@testset "Onehot" begin
-  include("onehot.jl")
+if VERSION >= v"1.2"
+  doctest(Flux)
 end
 
-@testset "Optimise" begin
-  include("optimise.jl")
-end
-
-@testset "Data" begin
-  include("data.jl")
-end
-
-@testset "Layers" begin
-  include("layers/basic.jl")
-  include("layers/normalisation.jl")
-  include("layers/stateless.jl")
-  include("layers/conv.jl")
-end
-
-@testset "CUDA" begin
-  if Flux.use_cuda[]
-    include("cuda/cuda.jl")
-  else
-    @warn "CUDA unavailable, not testing GPU support"
-  end
-end
-
-@static if VERSION >= v"1.4"
-  using Documenter
-  @testset "Docs" begin
-    DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive=true)
-    doctest(Flux)
-  end
 end