Merge branch 'master' into patch-6

2019-09-30 21:05:02 +05:30 · 2019-09-30 21:05:02 +05:30 · 2b30319a55
parent ec35e9cbaa e2b93bc78a
commit 2b30319a55
48 changed files with 897 additions and 1895 deletions
--- a/.gitattributes
+++ b/.gitattributes
@ -1 +1,2 @@
 paper/* linguist-documentation
+CITATION.bib linguist-detectable=false
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@ -5,7 +5,7 @@ variables:
  CI_IMAGE_TAG: 'cuda'

 include:
-  - 'https://raw.githubusercontent.com/JuliaGPU/gitlab-ci/master/templates/v3/common.yml'
+  - 'https://raw.githubusercontent.com/JuliaGPU/gitlab-ci/master/templates/v4/common.yml'

 .flux:
  extends: .test
@ -13,25 +13,39 @@ include:
    - julia -e 'using InteractiveUtils;
                versioninfo()'
    - mkdir $JULIA_DEPOT_PATH # Pkg3.jl#325
-    - julia -e 'using Pkg;
-                Pkg.add("CuArrays");'
    - julia --project -e 'using Pkg;
                          Pkg.instantiate();
                          Pkg.build();
                          Pkg.test(; coverage=true);'

 test:v1.0:
-  extends: .flux
-  variables:
-    CI_VERSION_TAG: 'v1.0'
-  only:
-    - staging
-    - trying
+   extends: .flux
+   variables:
+     CI_VERSION_TAG: 'v1.0'

 test:v1.1:
+   extends: .flux
+   variables:
+     CI_VERSION_TAG: 'v1.1'
+
+test:v1.2:
+   extends: .flux
+   variables:
+     CI_VERSION_TAG: 'v1.2'
+
+test:v1.3:
+   extends: .flux
+   variables:
+     CI_VERSION_TAG: 'v1.3'
+
+test:v1.0:
+   extends: .flux
+   variables:
+     CI_VERSION_TAG: 'v1.0'
+
+test:dev:
  extends: .flux
  variables:
-    CI_VERSION_TAG: 'v1.1'
-  only:
-    - staging
-    - trying
+    CI_VERSION_TAG: 'dev'
+
+  allow_failure: true
--- a/.travis.yml
+++ b/.travis.yml
@ -6,7 +6,7 @@ os:
  # - osx

 julia:
-  - 1.0
+  - 1.1
  - nightly

 matrix:
--- a/Manifest.toml
+++ b/Manifest.toml
@ -1,5 +1,11 @@
 # This file is machine-generated - editing it directly is not advised

+[[AbstractFFTs]]
+deps = ["LinearAlgebra"]
+git-tree-sha1 = "380e36c66edfa099cd90116b24c1ce8cafccac40"
+uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
+version = "0.4.1"
+
 [[AbstractTrees]]
 deps = ["Markdown", "Test"]
 git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b"
@ -7,10 +13,10 @@ uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
 version = "0.2.1"

 [[Adapt]]
-deps = ["LinearAlgebra", "Test"]
-git-tree-sha1 = "53d8fec4f662088c1202530e338a11a919407f3b"
+deps = ["LinearAlgebra"]
+git-tree-sha1 = "82dab828020b872fa9efd3abec1152b075bc7cbf"
 uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "0.4.2"
+version = "1.0.0"

 [[Base64]]
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
@ -22,34 +28,57 @@ uuid = "9e28174c-4ba2-5203-b857-d8d62c4213ee"
 version = "0.8.10"

 [[BinaryProvider]]
-deps = ["Libdl", "Pkg", "SHA", "Test"]
-git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e"
+deps = ["Libdl", "Logging", "SHA"]
+git-tree-sha1 = "c7361ce8a2129f20b0e05a89f7070820cfed6648"
 uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
-version = "0.5.3"
+version = "0.5.6"
+
+[[CEnum]]
+git-tree-sha1 = "62847acab40e6855a9b5905ccb99c2b5cf6b3ebb"
+uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
+version = "0.2.0"

 [[CSTParser]]
-deps = ["LibGit2", "Test", "Tokenize"]
-git-tree-sha1 = "437c93bc191cd55957b3f8dee7794b6131997c56"
+deps = ["Tokenize"]
+git-tree-sha1 = "c69698c3d4a7255bc1b4bc2afc09f59db910243b"
 uuid = "00ebfdb7-1f24-5e51-bd34-a7502290713f"
-version = "0.5.2"
+version = "0.6.2"
+
+[[CUDAapi]]
+deps = ["Libdl", "Logging"]
+git-tree-sha1 = "e063efb91cfefd7e6afd92c435d01398107a500b"
+uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
+version = "1.2.0"
+
+[[CUDAdrv]]
+deps = ["CUDAapi", "Libdl", "Printf"]
+git-tree-sha1 = "9ce99b5732c70e06ed97c042187baed876fb1698"
+uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
+version = "3.1.0"
+
+[[CUDAnative]]
+deps = ["Adapt", "CUDAapi", "CUDAdrv", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "Logging", "Printf", "TimerOutputs"]
+git-tree-sha1 = "52ae1ce10ebfa686e227655c47b19add89308623"
+uuid = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
+version = "2.3.1"

 [[CodecZlib]]
-deps = ["BinaryProvider", "Libdl", "Test", "TranscodingStreams"]
-git-tree-sha1 = "36bbf5374c661054d41410dc53ff752972583b9b"
+deps = ["BinaryProvider", "Libdl", "TranscodingStreams"]
+git-tree-sha1 = "05916673a2627dd91b4969ff8ba6941bc85a960e"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.5.2"
+version = "0.6.0"

 [[ColorTypes]]
-deps = ["FixedPointNumbers", "Random", "Test"]
-git-tree-sha1 = "f73b0e10f2a5756de7019818a41654686da06b09"
+deps = ["FixedPointNumbers", "Random"]
+git-tree-sha1 = "10050a24b09e8e41b951e9976b109871ce98d965"
 uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
-version = "0.7.5"
+version = "0.8.0"

 [[Colors]]
-deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Printf", "Reexport", "Test"]
-git-tree-sha1 = "9f0a0210450acb91c730b730a994f8eef1d3d543"
+deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Printf", "Reexport"]
+git-tree-sha1 = "c9c1845d6bf22e34738bee65c357a69f416ed5d1"
 uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
-version = "0.9.5"
+version = "0.9.6"

 [[CommonSubexpressions]]
 deps = ["Test"]
@ -63,17 +92,36 @@ git-tree-sha1 = "84aa74986c5b9b898b0d1acaf3258741ee64754f"
 uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
 version = "2.1.0"

+[[Conda]]
+deps = ["JSON", "VersionParsing"]
+git-tree-sha1 = "9a11d428dcdc425072af4aea19ab1e8c3e01c032"
+uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
+version = "1.3.0"
+
 [[Crayons]]
 deps = ["Test"]
 git-tree-sha1 = "f621b8ef51fd2004c7cf157ea47f027fdeac5523"
 uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
 version = "4.0.0"

+[[CuArrays]]
+deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "LinearAlgebra", "MacroTools", "NNlib", "Printf", "Random", "Requires", "SparseArrays", "TimerOutputs"]
+git-tree-sha1 = "45683305171430978c17f496969dc9b6d3094a51"
+repo-rev = "master"
+repo-url = "https://github.com/JuliaGPU/CuArrays.jl.git"
+uuid = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
+version = "1.3.0"
+
+[[DataAPI]]
+git-tree-sha1 = "8903f0219d3472543fc4b2f5ebaf675a07f817c0"
+uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
+version = "1.0.1"
+
 [[DataStructures]]
-deps = ["InteractiveUtils", "OrderedCollections", "Random", "Serialization", "Test"]
-git-tree-sha1 = "ca971f03e146cf144a9e2f2ce59674f5bf0e8038"
+deps = ["InteractiveUtils", "OrderedCollections"]
+git-tree-sha1 = "0809951a1774dc724da22d26e4289bbaab77809a"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.15.0"
+version = "0.17.0"

 [[Dates]]
 deps = ["Printf"]
@ -99,11 +147,22 @@ version = "0.0.10"
 deps = ["Random", "Serialization", "Sockets"]
 uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"

+[[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 = "8fba6ddaf66b45dec830233cea0aae43eb1261ad"
+uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
+version = "0.6.4"
+
 [[FixedPointNumbers]]
-deps = ["Test"]
-git-tree-sha1 = "b8045033701c3b10bf2324d7203404be7aef88ba"
+git-tree-sha1 = "d14a6fa5890ea3a7e5dcab6811114f132fec2b4b"
 uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
-version = "0.5.3"
+version = "0.6.1"

 [[ForwardDiff]]
 deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "InteractiveUtils", "LinearAlgebra", "NaNMath", "Random", "SparseArrays", "SpecialFunctions", "StaticArrays", "Test"]
@ -111,15 +170,39 @@ git-tree-sha1 = "4c4d727f1b7e0092134fabfab6396b8945c1ea5b"
 uuid = "f6369f11-7733-5829-9624-2563aa707210"
 version = "0.10.3"

+[[GPUArrays]]
+deps = ["Adapt", "FFTW", "FillArrays", "LinearAlgebra", "Printf", "Random", "Serialization", "StaticArrays", "Test"]
+git-tree-sha1 = "77e27264276fe97a7e7fb928bf8999a145abc018"
+uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
+version = "1.0.3"
+
+[[IRTools]]
+deps = ["InteractiveUtils", "MacroTools", "Test"]
+git-tree-sha1 = "e23faa71b8f54c3fdc99b230b9c2906cafdddca5"
+uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
+version = "0.2.3"
+
 [[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"
+
 [[Juno]]
 deps = ["Base64", "Logging", "Media", "Profile", "Test"]
-git-tree-sha1 = "4e4a8d43aa7ecec66cadaf311fbd1e5c9d7b9175"
+git-tree-sha1 = "30d94657a422d09cb97b6f86f04f750fa9c50df8"
 uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
-version = "0.7.0"
+version = "0.7.2"
+
+[[LLVM]]
+deps = ["CEnum", "Libdl", "Printf", "Unicode"]
+git-tree-sha1 = "4a05f742837779a00bd8c9a18da6817367c4245d"
+uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
+version = "1.3.0"

 [[LibGit2]]
 uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
@ -135,10 +218,10 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"

 [[MacroTools]]
-deps = ["CSTParser", "Compat", "DataStructures", "Test"]
-git-tree-sha1 = "daecd9e452f38297c686eba90dba2a6d5da52162"
+deps = ["CSTParser", "Compat", "DataStructures", "Test", "Tokenize"]
+git-tree-sha1 = "d6e9dedb8c92c3465575442da456aec15a89ff76"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.0"
+version = "0.5.1"

 [[Markdown]]
 deps = ["Base64"]
@ -151,10 +234,9 @@ uuid = "e89f7d12-3494-54d1-8411-f7d8b9ae1f27"
 version = "0.5.0"

 [[Missings]]
-deps = ["Dates", "InteractiveUtils", "SparseArrays", "Test"]
-git-tree-sha1 = "d1d2585677f2bd93a97cfeb8faa7a0de0f982042"
+git-tree-sha1 = "29858ce6c8ae629cf2d733bffa329619a1c843d0"
 uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-version = "0.4.0"
+version = "0.4.2"

 [[Mmap]]
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"
@ -177,6 +259,12 @@ git-tree-sha1 = "c4c13474d23c60d20a67b217f1d7f22a40edf8f1"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 version = "1.1.0"

+[[Parsers]]
+deps = ["Dates", "Test"]
+git-tree-sha1 = "ef0af6c8601db18c282d092ccbd2f01f3f0cd70b"
+uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
+version = "0.3.7"
+
 [[Pkg]]
 deps = ["Dates", "LibGit2", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
@ -233,26 +321,26 @@ deps = ["LinearAlgebra", "Random"]
 uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

 [[SpecialFunctions]]
-deps = ["BinDeps", "BinaryProvider", "Libdl", "Test"]
-git-tree-sha1 = "0b45dc2e45ed77f445617b99ff2adf0f5b0f23ea"
+deps = ["BinDeps", "BinaryProvider", "Libdl"]
+git-tree-sha1 = "3bdd374b6fd78faf0119b8c5d538788dbf910c6e"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "0.7.2"
+version = "0.8.0"

 [[StaticArrays]]
-deps = ["InteractiveUtils", "LinearAlgebra", "Random", "Statistics", "Test"]
-git-tree-sha1 = "3841b39ed5f047db1162627bf5f80a9cd3e39ae2"
+deps = ["LinearAlgebra", "Random", "Statistics"]
+git-tree-sha1 = "db23bbf50064c582b6f2b9b043c8e7e98ea8c0c6"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "0.10.3"
+version = "0.11.0"

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

 [[StatsBase]]
-deps = ["DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
-git-tree-sha1 = "8a0f4b09c7426478ab677245ab2b0b68552143c7"
+deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
+git-tree-sha1 = "c53e809e63fe5cf5de13632090bc3520649c9950"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.30.0"
+version = "0.32.0"

 [[Test]]
 deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
@ -265,22 +353,15 @@ uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 version = "0.5.0"

 [[Tokenize]]
-deps = ["Printf", "Test"]
-git-tree-sha1 = "3e83f60b74911d3042d3550884ca2776386a02b8"
+git-tree-sha1 = "dfcdbbfb2d0370716c815cbd6f8a364efb6f42cf"
 uuid = "0796e94c-ce3b-5d07-9a54-7f471281c624"
-version = "0.5.3"
-
-[[Tracker]]
-deps = ["Adapt", "DiffRules", "ForwardDiff", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics", "Test"]
-git-tree-sha1 = "0bec1b68c63a0e8a58d3944261cbf4cc9577c8a1"
-uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
-version = "0.2.0"
+version = "0.5.6"

 [[TranscodingStreams]]
 deps = ["Random", "Test"]
-git-tree-sha1 = "a25d8e5a28c3b1b06d3859f30757d43106791919"
+git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
 uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.9.4"
+version = "0.9.5"

 [[URIParser]]
 deps = ["Test", "Unicode"]
@ -295,8 +376,30 @@ uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 [[Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

+[[VersionParsing]]
+deps = ["Compat"]
+git-tree-sha1 = "c9d5aa108588b978bd859554660c8a5c4f2f7669"
+uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
+version = "1.1.3"
+
 [[ZipFile]]
-deps = ["BinaryProvider", "Libdl", "Printf", "Test"]
-git-tree-sha1 = "5f6f663890dfb9bad6af75a86a43f67904e5050e"
+deps = ["BinaryProvider", "Libdl", "Printf"]
+git-tree-sha1 = "580ce62b6c14244916cc28ad54f8a2e2886f843d"
 uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.8.1"
+version = "0.8.3"
+
+[[Zygote]]
+deps = ["DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
+git-tree-sha1 = "38241b40ebd8748bcacad5e6c7ba3ab3cc7a15c9"
+repo-rev = "master"
+repo-url = "https://github.com/FluxML/Zygote.jl.git"
+uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
+version = "0.3.4"
+
+[[ZygoteRules]]
+deps = ["MacroTools"]
+git-tree-sha1 = "c4c29b30b8ff3be13d4244e78be7df2a42bc54d0"
+repo-rev = "master"
+repo-url = "https://github.com/FluxML/ZygoteRules.jl.git"
+uuid = "700de1a5-db45-46bc-99cf-38207098b444"
+version = "0.2.0"
--- a/NEWS.md
+++ b/NEWS.md
@ -1,6 +1,7 @@
 # v0.9.0
 * [Depthwise convolutional layer API changes](https://github.com/FluxML/Flux.jl/pull/756) from `in => mult` channel specification to `in => out` channel specification, and deprecates implicit `out` constructor.
 * New [SkipConnection](https://github.com/FluxML/Flux.jl/pull/446), which can be used to train residual neural network architectures.
+* New [RADAM](https://github.com/FluxML/Flux.jl/pull/842) optimiser.

 # v0.8.0

--- a/Project.toml
+++ b/Project.toml
@ -1,35 +1,40 @@
 name = "Flux"
 uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-version = "0.8.3"
+version = "0.9.0"

 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+CUDAapi = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
 CodecZlib = "944b1d66-785c-5afd-91f1-9de20f533193"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
+CuArrays = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 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"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SHA = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"

 [compat]
+CUDAapi = "1.1"
+CuArrays = "1.2"
 NNlib = "0.6"
-Tracker = "0.2"
-julia = "0.7, 1"
+Zygote = "0.3"
+julia = "1"

 [extras]
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

 [targets]
-test = ["Test"]
+test = ["Test", "Documenter"]
--- a/13
+++ b/13
@ -1,13 +0,0 @@
-julia 1.0
-Juno
-MacroTools 0.3.3
-NNlib
-Requires
-Adapt 0.4
-CodecZlib
-Colors
-ZipFile
-AbstractTrees
-Reexport
-StatsBase
-Tracker
--- a/docs/Manifest.toml
+++ b/docs/Manifest.toml
@ -1,205 +1,56 @@
 # This file is machine-generated - editing it directly is not advised

-[[AbstractTrees]]
-deps = ["Markdown", "Test"]
-git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b"
-uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
-version = "0.2.1"
-
-[[Adapt]]
-deps = ["LinearAlgebra", "Test"]
-git-tree-sha1 = "53d8fec4f662088c1202530e338a11a919407f3b"
-uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "0.4.2"
-
 [[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", "Pkg", "SHA", "Test"]
-git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e"
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-version = "0.5.3"
-
-[[CSTParser]]
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-uuid = "00ebfdb7-1f24-5e51-bd34-a7502290713f"
-version = "0.5.2"
-
-[[CodecZlib]]
-deps = ["BinaryProvider", "Libdl", "Test", "TranscodingStreams"]
-git-tree-sha1 = "36bbf5374c661054d41410dc53ff752972583b9b"
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-version = "0.5.2"
-
-[[ColorTypes]]
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-version = "0.7.5"
-
-[[Colors]]
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-
-[[CommonSubexpressions]]
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-version = "0.2.0"
-
-[[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 = "84aa74986c5b9b898b0d1acaf3258741ee64754f"
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-version = "2.1.0"
-
-[[Crayons]]
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-
-[[DataStructures]]
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-version = "0.15.0"
-
 [[Dates]]
 deps = ["Printf"]
 uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"

-[[DelimitedFiles]]
-deps = ["Mmap"]
-uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-
-[[DiffResults]]
-deps = ["Compat", "StaticArrays"]
-git-tree-sha1 = "34a4a1e8be7bc99bc9c611b895b5baf37a80584c"
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-version = "0.0.4"
-
-[[DiffRules]]
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-version = "0.0.10"
-
 [[Distributed]]
 deps = ["Random", "Serialization", "Sockets"]
 uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"

 [[DocStringExtensions]]
 deps = ["LibGit2", "Markdown", "Pkg", "Test"]
-git-tree-sha1 = "4d30e889c9f106a51ffa4791a88ffd4765bf20c3"
+git-tree-sha1 = "0513f1a8991e9d83255e0140aace0d0fc4486600"
 uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
-version = "0.7.0"
+version = "0.8.0"

 [[Documenter]]
-deps = ["Base64", "DocStringExtensions", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "Pkg", "REPL", "Random", "Test", "Unicode"]
-git-tree-sha1 = "13a6d15102410d8e70146533b759fc48d844a1d0"
+deps = ["Base64", "DocStringExtensions", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "REPL", "Test", "Unicode"]
+git-tree-sha1 = "c61d6eedbc3c4323c08b64af12d29c8ee0fcbb5f"
 uuid = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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-
-[[FixedPointNumbers]]
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-version = "0.5.3"
-
-[[Flux]]
-deps = ["AbstractTrees", "Adapt", "CodecZlib", "Colors", "DelimitedFiles", "Juno", "LinearAlgebra", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SHA", "Statistics", "StatsBase", "Tracker", "ZipFile"]
-path = ".."
-uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-version = "0.8.2+"
-
-[[ForwardDiff]]
-deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "InteractiveUtils", "LinearAlgebra", "NaNMath", "Random", "SparseArrays", "SpecialFunctions", "StaticArrays", "Test"]
-git-tree-sha1 = "4c4d727f1b7e0092134fabfab6396b8945c1ea5b"
-uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "0.10.3"
+version = "0.23.2"

 [[InteractiveUtils]]
 deps = ["Markdown"]
 uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"

 [[JSON]]
-deps = ["Dates", "Distributed", "Mmap", "Sockets", "Test", "Unicode"]
-git-tree-sha1 = "1f7a25b53ec67f5e9422f1f551ee216503f4a0fa"
+deps = ["Dates", "Mmap", "Parsers", "Unicode"]
+git-tree-sha1 = "b34d7cef7b337321e97d22242c3c2b91f476748e"
 uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-version = "0.20.0"
-
-[[Juno]]
-deps = ["Base64", "Logging", "Media", "Profile", "Test"]
-git-tree-sha1 = "4e4a8d43aa7ecec66cadaf311fbd1e5c9d7b9175"
-uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
-version = "0.7.0"
+version = "0.21.0"

 [[LibGit2]]
 uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"

-[[Libdl]]
-uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
-
-[[LinearAlgebra]]
-deps = ["Libdl"]
-uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-
 [[Logging]]
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"

-[[MacroTools]]
-deps = ["CSTParser", "Compat", "DataStructures", "Test"]
-git-tree-sha1 = "daecd9e452f38297c686eba90dba2a6d5da52162"
-uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.0"
-
 [[Markdown]]
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"

-[[Media]]
-deps = ["MacroTools", "Test"]
-git-tree-sha1 = "75a54abd10709c01f1b86b84ec225d26e840ed58"
-uuid = "e89f7d12-3494-54d1-8411-f7d8b9ae1f27"
-version = "0.5.0"
-
-[[Missings]]
-deps = ["Dates", "InteractiveUtils", "SparseArrays", "Test"]
-git-tree-sha1 = "d1d2585677f2bd93a97cfeb8faa7a0de0f982042"
-uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-version = "0.4.0"
-
 [[Mmap]]
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"

-[[NNlib]]
-deps = ["Libdl", "LinearAlgebra", "Requires", "Statistics", "TimerOutputs"]
-git-tree-sha1 = "0c667371391fc6bb31f7f12f96a56a17098b3de8"
-uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.6.0"
-
-[[NaNMath]]
-deps = ["Compat"]
-git-tree-sha1 = "ce3b85e484a5d4c71dd5316215069311135fa9f2"
-uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
-version = "0.3.2"
-
-[[OrderedCollections]]
-deps = ["Random", "Serialization", "Test"]
-git-tree-sha1 = "c4c13474d23c60d20a67b217f1d7f22a40edf8f1"
-uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.1.0"
+[[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"]
@ -209,10 +60,6 @@ uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 deps = ["Unicode"]
 uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"

-[[Profile]]
-deps = ["Printf"]
-uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"
-
 [[REPL]]
 deps = ["InteractiveUtils", "Markdown", "Sockets"]
 uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
@ -221,106 +68,22 @@ uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
 deps = ["Serialization"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

-[[Reexport]]
-deps = ["Pkg"]
-git-tree-sha1 = "7b1d07f411bc8ddb7977ec7f377b97b158514fe0"
-uuid = "189a3867-3050-52da-a836-e630ba90ab69"
-version = "0.2.0"
-
-[[Requires]]
-deps = ["Test"]
-git-tree-sha1 = "f6fbf4ba64d295e146e49e021207993b6b48c7d1"
-uuid = "ae029012-a4dd-5104-9daa-d747884805df"
-version = "0.5.2"
-
 [[SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"

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

-[[SharedArrays]]
-deps = ["Distributed", "Mmap", "Random", "Serialization"]
-uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
-
 [[Sockets]]
 uuid = "6462fe0b-24de-5631-8697-dd941f90decc"

-[[SortingAlgorithms]]
-deps = ["DataStructures", "Random", "Test"]
-git-tree-sha1 = "03f5898c9959f8115e30bc7226ada7d0df554ddd"
-uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
-version = "0.3.1"
-
-[[SparseArrays]]
-deps = ["LinearAlgebra", "Random"]
-uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-
-[[SpecialFunctions]]
-deps = ["BinDeps", "BinaryProvider", "Libdl", "Test"]
-git-tree-sha1 = "0b45dc2e45ed77f445617b99ff2adf0f5b0f23ea"
-uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "0.7.2"
-
-[[StaticArrays]]
-deps = ["InteractiveUtils", "LinearAlgebra", "Random", "Statistics", "Test"]
-git-tree-sha1 = "3841b39ed5f047db1162627bf5f80a9cd3e39ae2"
-uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "0.10.3"
-
-[[Statistics]]
-deps = ["LinearAlgebra", "SparseArrays"]
-uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-
-[[StatsBase]]
-deps = ["DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
-git-tree-sha1 = "8a0f4b09c7426478ab677245ab2b0b68552143c7"
-uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.30.0"
-
 [[Test]]
 deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

-[[TimerOutputs]]
-deps = ["Crayons", "Printf", "Test", "Unicode"]
-git-tree-sha1 = "b80671c06f8f8bae08c55d67b5ce292c5ae2660c"
-uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
-version = "0.5.0"
-
-[[Tokenize]]
-deps = ["Printf", "Test"]
-git-tree-sha1 = "3e83f60b74911d3042d3550884ca2776386a02b8"
-uuid = "0796e94c-ce3b-5d07-9a54-7f471281c624"
-version = "0.5.3"
-
-[[Tracker]]
-deps = ["Adapt", "DiffRules", "ForwardDiff", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics", "Test"]
-git-tree-sha1 = "0bec1b68c63a0e8a58d3944261cbf4cc9577c8a1"
-uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
-version = "0.2.0"
-
-[[TranscodingStreams]]
-deps = ["Random", "Test"]
-git-tree-sha1 = "a25d8e5a28c3b1b06d3859f30757d43106791919"
-uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.9.4"
-
-[[URIParser]]
-deps = ["Test", "Unicode"]
-git-tree-sha1 = "6ddf8244220dfda2f17539fa8c9de20d6c575b69"
-uuid = "30578b45-9adc-5946-b283-645ec420af67"
-version = "0.4.0"
-
 [[UUIDs]]
 deps = ["Random", "SHA"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

 [[Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
-
-[[ZipFile]]
-deps = ["BinaryProvider", "Libdl", "Printf", "Test"]
-git-tree-sha1 = "5f6f663890dfb9bad6af75a86a43f67904e5050e"
-uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.8.1"
--- a/docs/Project.toml
+++ b/docs/Project.toml
@ -1,4 +1,2 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
--- a/docs/make.jl
+++ b/docs/make.jl
@ -1,12 +1,13 @@
+using Pkg;
+Pkg.activate(joinpath(@__DIR__, "..")); Pkg.instantiate()
+Pkg.activate(); Pkg.instantiate()
+
+pushfirst!(LOAD_PATH, joinpath(@__DIR__, ".."))
+
 using Documenter, Flux, NNlib

 makedocs(modules=[Flux, NNlib],
-         doctest = true,
-         analytics = "UA-36890222-9",
         sitename = "Flux",
-         # Uncomment below for local build
-         #format = Documenter.HTML(prettyurls = false),
-         assets = ["assets/flux.css"],
         pages = ["Home" => "index.md",
                  "Building Models" =>
                    ["Basics" => "models/basics.md",
@ -20,8 +21,9 @@ makedocs(modules=[Flux, NNlib],
                  "GPU Support" => "gpu.md",
                  "Saving & Loading" => "saving.md",
                  "Performance Tips" => "performance.md",
-                  "Internals" =>
-                    ["Backpropagation" => "internals/tracker.md"],
-                  "Community" => "community.md"])
+                  "Community" => "community.md"],
+         format = Documenter.HTML(assets = ["assets/flux.css"],
+                                  analytics = "UA-36890222-9",
+                                  prettyurls = haskey(ENV, "CI")))

 deploydocs(repo = "github.com/FluxML/Flux.jl.git")
--- a/docs/src/community.md
+++ b/docs/src/community.md
@ -1,5 +1,5 @@
 # Community

-All Flux users are welcome to join our community on the [Julia forum](https://discourse.julialang.org/), the [slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866) (channel #machine-learning), or Flux's [Gitter](https://gitter.im/FluxML/Lobby). If you have questions or issues we'll try to help you out.
+All Flux users are welcome to join our community on the [Julia forum](https://discourse.julialang.org/), or the [slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866) (channel #machine-learning). If you have questions or issues we'll try to help you out.

 If you're interested in hacking on Flux, the [source code](https://github.com/FluxML/Flux.jl) is open and easy to understand -- it's all just the same Julia code you work with normally. You might be interested in our [intro issues](https://github.com/FluxML/Flux.jl/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) to get started.
--- a/docs/src/gpu.md
+++ b/docs/src/gpu.md
@ -1,14 +1,6 @@
 # GPU Support

-## Installation
-
-To get GPU support for NVIDIA graphics cards, you need to install `CuArrays.jl`
-
-**Steps needed**
-
-1. Install [NVIDIA toolkit](https://developer.nvidia.com/cuda-downloads)
-2. Install [NVIDIA cuDNN library](https://developer.nvidia.com/cudnn)
-3. In Julia's terminal run `]add CuArrays`
+NVIDIA GPU support should work out of the box on systems with CUDA and CUDNN installed. For more details see the [CuArrays](https://github.com/JuliaGPU/CuArrays.jl) readme.

 ## GPU Usage

@ -33,16 +25,16 @@ loss(x, y) # ~ 3

 Note that we convert both the parameters (`W`, `b`) and the data set (`x`, `y`) to cuda arrays. Taking derivatives and training works exactly as before.

-If you define a structured model, like a `Dense` layer or `Chain`, you just need to convert the internal parameters. Flux provides `mapleaves`, which allows you to alter all parameters of a model at once.
+If you define a structured model, like a `Dense` layer or `Chain`, you just need to convert the internal parameters. Flux provides `fmap`, which allows you to alter all parameters of a model at once.

 ```julia
 d = Dense(10, 5, σ)
-d = mapleaves(cu, d)
+d = fmap(cu, d)
 d.W # Tracked CuArray
 d(cu(rand(10))) # CuArray output

 m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
-m = mapleaves(cu, m)
+m = fmap(cu, m)
 d(cu(rand(10)))
 ```

--- a/docs/src/internals/tracker.md
+++ b/docs/src/internals/tracker.md
@ -1,184 +0,0 @@
-# Flux.Tracker
-
-Backpropagation, or reverse-mode automatic differentiation, is handled by the `Flux.Tracker` module.
-
-```julia
-julia> using Flux.Tracker
-```
-
-Here we discuss some more advanced uses of this module, as well as covering its internals.
-
-## Taking Gradients
-
-In the [basics section](../models/basics.md) we covered basic usage of the `gradient` function.
-
-```julia
-using Flux.Tracker
-
-Tracker.gradient((a, b) -> a*b, 2, 3) # (3.0 (tracked), 2.0 (tracked))
-```
-
-`gradient` is actually just a thin wrapper around the backpropagator-based interface, `forward`.
-
-```julia
-using Flux.Tracker: forward
-
-y, back = forward((a, b) -> a*b, 2, 3) # (6.0 (tracked), Flux.Tracker.#9)
-
-back(1) # (3.0 (tracked), 2.0 (tracked))
-```
-
-The `forward` function returns two results. The first, `y`, is the original value of the function (perhaps with tracking applied). The second, `back`, is a new function which, given a sensitivity, returns the sensitivity of the inputs to `forward` (we call this a "backpropagator"). One use of this interface is to provide custom sensitivities when outputs are not scalar.
-
-```julia
-julia> y, back = forward((a, b) -> a.*b, [1,2,3],[4,5,6])
-(param([4.0, 10.0, 18.0]), Flux.Tracker.#9)
-
-julia> back([1,1,1])
-(param([4.0, 5.0, 6.0]), param([1.0, 2.0, 3.0]))
-```
-
-We can also take gradients in-place. This can be useful if you only care about first-order gradients.
-
-```julia
-a, b = param(2), param(3)
-
-c = a*b # 6.0 (tracked)
-
-Tracker.back!(c)
-
-Tracker.grad(a), Tracker.grad(b) # (3.0, 2.0)
-```
-
-## Tracked Arrays
-
-The `param` function converts a normal Julia array into a new object that, while behaving like an array, tracks extra information that allows us to calculate derivatives. For example, say we multiply two parameters:
-
-```julia
-julia> W = param([1 2; 3 4])
-Tracked 2×2 Array{Float64,2}:
- 1.0  2.0
- 3.0  4.0
-
-julia> x = param([5, 6])
-Tracked 2-element Array{Float64,1}:
- 5.0
- 6.0
-
-julia> y = W*x
-Tracked 2-element Array{Float64,1}:
- 17.0
- 39.0
-```
-
-The output `y` is also a `TrackedArray` object. We can now backpropagate sensitivities to `W` and `x` via the `back!` function, and see the gradients accumulated in the `W` and `x` tracked arrays:
-
-```julia
-julia> Tracker.back!(y, [1, -1])
-
-julia> W.grad
-2×2 Array{Float64,2}:
- 5.0   6.0
-5.0  -6.0
-
-julia> x.grad
-2-element Array{Float64,1}:
- -2.0
- -2.0
-```
-
-You may sometimes want to drop derivative information and just get the plain value back. You can do this by calling `Tracker.data(W)`.
-
-## Custom Gradients
-
-We can hook in to the processes above to implement custom gradients for a function or kernel. For a toy example, imagine a custom implementation of `minus`:
-
-```julia
-minus(a, b) = a - b
-```
-
-Firstly, we must tell the tracker system to stop when it sees a call to `minus`, and record it. We can do this using dispatch:
-
-```julia
-using Flux.Tracker: TrackedArray, track, @grad
-
-minus(a::TrackedArray, b::TrackedArray) = track(minus, a, b)
-```
-
-`track` takes care of building a new `Tracked` object and recording the operation on the tape. We just need to provide a gradient definition.
-
-```julia
-@grad function minus(a, b)
-  return minus(data(a), data(b)), Δ -> (Δ, -Δ)
-end
-```
-
-This is essentially just a way of overloading the `forward` function we saw above. We strip tracking from `a` and `b` so that we are calling the original definition of `minus` (otherwise, we'd just try to track the call again and hit an infinite regress).
-
-Note that in the backpropagator we don't call `data(a)`; we *do* in fact want to track this, since nest AD will take a derivative through the backpropagator itself. For example, the gradient of `*` might look like this.
-
-```julia
-@grad a * b = data(a)*data(b), Δ -> (Δ*b, a*Δ)
-```
-
-We can then calculate the first derivative of `minus` as follows:
-
-```julia
-a = param([1,2,3])
-b = param([3,2,1])
-
-c = minus(a, b)  # [-2.0 (tracked), 0.0 (tracked), 2.0 (tracked)]
-
-Tracker.back!(c, 1)
-Tracker.grad(a)  # [1.00, 1.00, 1.00]
-Tracker.grad(b)  # [-1.00, -1.00, -1.00]
-```
-
-For multi-argument functions with custom gradients, you likely want to catch not just `minus(::TrackedArray, ::TrackedArray)` but also `minus(::Array, TrackedArray)` and so on. To do so, just define those extra signatures as needed:
-
-```julia
-minus(a::AbstractArray, b::TrackedArray) = Tracker.track(minus, a, b)
-minus(a::TrackedArray, b::AbstractArray) = Tracker.track(minus, a, b)
-```
-
-## Tracked Internals
-
-All `Tracked*` objects (`TrackedArray`, `TrackedReal`) are light wrappers around the `Tracked` type, which you can access via the `.tracker` field.
-
-```julia
-julia> x.tracker
-Flux.Tracker.Tracked{Array{Float64,1}}(0x00000000, Flux.Tracker.Call{Nothing,Tuple{}}(nothing, ()), true, [5.0, 6.0], [-2.0, -2.0])
-```
-
-The `Tracker` stores the gradient of a given object, which we've seen before.
-
-```julia
-julia> x.tracker.grad
-2-element Array{Float64,1}:
- -2.0
- -2.0
-```
-
-The tracker also contains a `Call` object, which simply represents a function call that was made at some point during the forward pass. For example, the `+` call would look like this:
-
-```julia
-julia> Tracker.Call(+, 1, 2)
-Flux.Tracker.Call{Base.#+,Tuple{Int64,Int64}}(+, (1, 2))
-```
-
-In the case of the `y` we produced above, we can see that it stores the call that produced it -- that is, `W*x`.
-
-```julia
-julia> y.tracker.f
-Flux.Tracker.Call{...}(*, (param([1.0 2.0; 3.0 4.0]), param([5.0, 6.0])))
-```
-
-Notice that because the arguments to the call may also be tracked arrays, storing their own calls, this means that `Tracker` ends up forming a data structure that records everything that happened during the forward pass (often known as a *tape*).
-
-When we call `back!(y, [1, -1])`, the sensitivities `[1, -1]` simply get forwarded to `y`'s call (`*`), effectively calling
-
-```julia
-Tracker.back(*, [1, -1], W, x)
-```
-
-which in turn calculates the sensitivities of the arguments (`W` and `x`) and back-propagates through their calls. This is recursive, so it will walk the entire program graph and propagate gradients to the original model parameters.
--- a/docs/src/models/basics.md
+++ b/docs/src/models/basics.md
@ -5,55 +5,56 @@
 Flux's core feature is taking gradients of Julia code. The `gradient` function takes another Julia function `f` and a set of arguments, and returns the gradient with respect to each argument. (It's a good idea to try pasting these examples in the Julia terminal.)

 ```jldoctest basics
-julia> using Flux.Tracker
+julia> using Flux

 julia> f(x) = 3x^2 + 2x + 1;

-julia> df(x) = Tracker.gradient(f, x; nest = true)[1]; # df/dx = 6x + 2
+julia> df(x) = gradient(f, x)[1]; # df/dx = 6x + 2

 julia> df(2)
-14.0 (tracked)
+14

-julia> d2f(x) = Tracker.gradient(df, x; nest = true)[1]; # d²f/dx² = 6
+julia> d2f(x) = gradient(df, x)[1]; # d²f/dx² = 6

 julia> d2f(2)
-6.0 (tracked)
+6
 ```

-(We'll learn more about why these numbers show up as `(tracked)` below.)
-
-When a function has many parameters, we can pass them all in explicitly:
+When a function has many parameters, we can get gradients of each one at the same time:

 ```jldoctest basics
-julia> f(W, b, x) = W * x + b;
+julia> f(x, y) = sum((x .- y).^2);

-julia> Tracker.gradient(f, 2, 3, 4)
-(4.0 (tracked), 1.0 (tracked), 2.0 (tracked))
+julia> gradient(f, [2, 1], [2, 0])
+([0, 2], [0, -2])
 ```

-But machine learning models can have *hundreds* of parameters! Flux offers a nice way to handle this. We can tell Flux to treat something as a parameter via `param`. Then we can collect these together and tell `gradient` to collect the gradients of all `params` at once.
+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> W = param(2) 
-2.0 (tracked)
+julia> x = [2, 1];

-julia> b = param(3)
-3.0 (tracked)
+julia> y = [2, 0];

-julia> f(x) = W * x + b;
+julia> gs = gradient(params(x, y)) do
+         f(x, y)
+       end
+Grads(...)

-julia> grads = Tracker.gradient(() -> f(4), params(W, b));
+julia> gs[x]
+2-element Array{Int64,1}:
+ 0
+ 2

-julia> grads[W]
-4.0 (tracked)
-
-julia> grads[b]
-1.0 (tracked)
+julia> gs[y]
+2-element Array{Int64,1}:
+  0
+ -2
 ```

-There are a few things to notice here. Firstly, `W` and `b` now show up as *tracked*. Tracked things behave like normal numbers or arrays, but keep records of everything you do with them, allowing Flux to calculate their gradients. `gradient` takes a zero-argument function; no arguments are necessary because the `params` tell it what to differentiate.
+Here, `gradient` takes a zero-argument function; no arguments are necessary because the `params` tell it what to differentiate.

 This will come in really handy when dealing with big, complicated models. For now, though, let's start with something simple.

@ -76,26 +77,20 @@ x, y = rand(5), rand(2) # Dummy data
 loss(x, y) # ~ 3
 ```

-To improve the prediction we can take the gradients of `W` and `b` with respect to the loss and perform gradient descent. Let's tell Flux that `W` and `b` are parameters, just like we did above.
+To improve the prediction we can take the gradients of `W` and `b` with respect to the loss and perform gradient descent.

 ```julia
-using Flux.Tracker
+using Flux

-W = param(W)
-b = param(b)
-
-gs = Tracker.gradient(() -> loss(x, y), params(W, b))
+gs = gradient(() -> loss(x, y), params(W, b))
 ```

-Now that we have gradients, we can pull them out and update `W` to train the model. The `update!(W, Δ)` function applies `W = W + Δ`, which we can use for gradient descent.
+Now that we have gradients, we can pull them out and update `W` to train the model.

 ```julia
-using Flux.Tracker: update!
+W̄ = gs[W]

-Δ = gs[W]
-
-# Update the parameter and reset the gradient
-update!(W, -0.1Δ)
+W .-= 0.1 .* W̄

 loss(x, y) # ~ 2.5
 ```
@ -111,12 +106,12 @@ It's common to create more complex models than the linear regression above. For
 ```julia
 using Flux

-W1 = param(rand(3, 5))
-b1 = param(rand(3))
+W1 = rand(3, 5)
+b1 = rand(3)
 layer1(x) = W1 * x .+ b1

-W2 = param(rand(2, 3))
-b2 = param(rand(2))
+W2 = rand(2, 3)
+b2 = rand(2)
 layer2(x) = W2 * x .+ b2

 model(x) = layer2(σ.(layer1(x)))
@ -128,8 +123,8 @@ This works but is fairly unwieldy, with a lot of repetition – especially as we

 ```julia
 function linear(in, out)
-  W = param(randn(out, in))
-  b = param(randn(out))
+  W = randn(out, in)
+  b = randn(out)
  x -> W * x .+ b
 end

@ -150,7 +145,7 @@ struct Affine
 end

 Affine(in::Integer, out::Integer) =
-  Affine(param(randn(out, in)), param(randn(out)))
+  Affine(randn(out, in), randn(out))

 # Overload call, so the object can be used as a function
 (m::Affine)(x) = m.W * x .+ m.b
@ -220,7 +215,7 @@ m(5) # => 26
 Flux provides a set of helpers for custom layers, which you can enable by calling

 ```julia
-Flux.@treelike Affine
+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).
--- a/docs/src/models/layers.md
+++ b/docs/src/models/layers.md
@ -59,7 +59,6 @@ swish
 These layers don't affect the structure of the network but may improve training times or reduce overfitting.

 ```@docs
-Flux.testmode!
 BatchNorm
 Dropout
 AlphaDropout
--- a/docs/src/models/recurrence.md
+++ b/docs/src/models/recurrence.md
@ -101,26 +101,4 @@ m = Chain(LSTM(10, 15), Dense(15, 5))
 m.(seq)
 ```

-## Truncating Gradients
-
-By default, calculating the gradients in a recurrent layer involves its entire history. For example, if we call the model on 100 inputs, we'll have to calculate the gradient for those 100 calls. If we then calculate another 10 inputs we have to calculate 110 gradients – this accumulates and quickly becomes expensive.
-
-To avoid this we can *truncate* the gradient calculation, forgetting the history.
-
-```julia
-truncate!(m)
-```
-
-Calling `truncate!` wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation.
-
-`truncate!` makes sense when you are working with multiple chunks of a large sequence, but we may also want to work with a set of independent sequences. In this case the hidden state should be completely reset to its original value, throwing away any accumulated information. `reset!` does this for you.
-
-In general, when training with recurrent layers in your model, you'll want to call `reset!` or `truncate!` for each loss calculation:
-
-```julia
-function loss(x,y)
-  l = Flux.mse(m(x), y)
-  Flux.reset!(m)
-  return l
-end
-```
+Finally, we can reset the hidden state of the cell back to its initial value using `reset!(m)`.
--- a/docs/src/models/regularisation.md
+++ b/docs/src/models/regularisation.md
@ -15,6 +15,8 @@ loss(x, y) = crossentropy(softmax(m(x)), y)
 We can regularise this by taking the (L2) norm of the parameters, `m.W` and `m.b`.

 ```julia
+using LinearAlgebra
+
 penalty() = norm(m.W) + norm(m.b)
 loss(x, y) = crossentropy(softmax(m(x)), y) + penalty()
 ```
@ -48,15 +50,17 @@ loss(rand(28^2), rand(10))
 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)
-Chain(Dense(10, 5, NNlib.σ), Dense(5, 2), NNlib.softmax)
+Chain(Dense(10, 5, σ), Dense(5, 2), softmax)

 julia> activations(c, rand(10))
 3-element Array{Any,1}:
- param([0.71068, 0.831145, 0.751219, 0.227116, 0.553074])
- param([0.0330606, -0.456104])
- param([0.61991, 0.38009])
+ Float32[0.84682214, 0.6704139, 0.42177814, 0.257832, 0.36255655]
+ Float32[0.1501253, 0.073269576]                                 
+ Float32[0.5192045, 0.48079553]                                  

 julia> sum(norm, ans)
-2.639678767773633 (tracked)
+2.1166067f0
 ```
--- a/docs/src/performance.md
+++ b/docs/src/performance.md
@ -14,8 +14,8 @@ Which means allocations occur much faster.
 And you use less memory.


-## Make sure your custom activation functions preserve the type of their inputs
-Not only should your activation functions be [type-stable](https://docs.julialang.org/en/v1/manual/performance-tips/#Write-%22type-stable%22-functions-1),
+## 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.

 A very artificial example using an activation function like
@ -26,6 +26,7 @@ A very artificial example using an activation function like

 will result in performance on `Float32` input orders of magnitude slower than the normal `tanh` would,
 because it results in having to use slow mixed type multiplication in the dense layers.
+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
@ -60,7 +61,7 @@ end

 It is much faster to concatenate them into a matrix,
 as this will hit BLAS matrix-matrix multiplication, which is much faster than the equivalent sequence of matrix-vector multiplications.
-Even though this means allocating new memory to store them contiguously.
+The improvement is enough that it is worthwhile allocating new memory to store them contiguously.

 ```julia
 x_batch = reduce(hcat, xs)
--- a/docs/src/saving.md
+++ b/docs/src/saving.md
@ -53,7 +53,7 @@ julia> using Flux
 julia> model = Chain(Dense(10,5,relu),Dense(5,2),softmax)
 Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NNlib.softmax)

-julia> weights = Tracker.data.(params(model));
+julia> weights = params(model);

 julia> using BSON: @save

--- a/docs/src/training/optimisers.md
+++ b/docs/src/training/optimisers.md
@ -3,25 +3,25 @@
 Consider a [simple linear regression](../models/basics.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.

 ```julia
-using Flux, Flux.Tracker
+using Flux

-W = param(rand(2, 5))
-b = param(rand(2))
+W = rand(2, 5)
+b = rand(2)

-predict(x) = W*x .+ b
+predict(x) = (W * x) .+ b
 loss(x, y) = sum((predict(x) .- y).^2)

 x, y = rand(5), rand(2) # Dummy data
 l = loss(x, y) # ~ 3

 θ = Params([W, b])
-grads = Tracker.gradient(() -> loss(x, y), θ)
+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.Tracker: grad, update!
+using Flux: update!

 η = 0.1 # Learning Rate
 for p in (W, b)
--- a/src/Flux.jl
+++ b/src/Flux.jl
@ -3,30 +3,39 @@ module Flux
 # Zero Flux Given

 using Base: tail
-using MacroTools, Juno, Requires, Reexport, Statistics, Random
+using Zygote, MacroTools, Juno, Reexport, Statistics, Random
 using MacroTools: @forward
+@reexport using NNlib
+using Zygote: Params, @adjoint, gradient, pullback
+export gradient

 export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,
       DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm,
-       SkipConnection,
-       params, mapleaves, cpu, gpu, f32, f64
-
-@reexport using NNlib
-
-using Tracker
-using Tracker: data
-export Tracker, TrackedArray, TrackedVector, TrackedMatrix, param
+       SkipConnection, params, fmap, cpu, gpu, f32, f64

 include("optimise/Optimise.jl")
 using .Optimise
 using .Optimise: @epochs
 export SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
  ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM,
-  ADAMW, InvDecay, ExpDecay, WeightDecay
+  ADAMW, RADAM, InvDecay, ExpDecay, WeightDecay
+
+using CUDAapi
+if has_cuda()
+  try
+    using CuArrays
+    @eval has_cuarrays() = true
+  catch ex
+    @warn "CUDA is installed, but CuArrays.jl fails to load" exception=(ex,catch_backtrace())
+    @eval has_cuarrays() = false
+  end
+else
+  has_cuarrays() = false
+end

 include("utils.jl")
 include("onehot.jl")
-include("treelike.jl")
+include("functor.jl")

 include("layers/stateless.jl")
 include("layers/basic.jl")
@ -36,6 +45,10 @@ include("layers/normalise.jl")

 include("data/Data.jl")

-@init @require CuArrays="3a865a2d-5b23-5a0f-bc46-62713ec82fae" include("cuda/cuda.jl")
+include("deprecations.jl")
+
+if has_cuarrays()
+  include("cuda/cuda.jl")
+end

 end # module
--- a/src/cuda/cuda.jl
+++ b/src/cuda/cuda.jl
@ -1,38 +1,13 @@
 module CUDA

 using ..CuArrays
-import ..CuArrays.CUDAdrv: CuPtr, CU_NULL
-using Pkg.TOML

-function version_check()
-  major_version = 1
-  project = joinpath(dirname(pathof(CuArrays)), "../Project.toml")
-  project = TOML.parse(String(read(project)))
-  version = VersionNumber(get(project, "version", "0.0.0"))
-  if version.major != major_version
-    @warn """
-    Flux is only supported with CuArrays v$major_version.x.
-    Try running `] pin CuArrays@$major_version`.
-    """
-  end
-end
-
-version_check()
-
-if !applicable(CuArray{UInt8}, undef, 1)
-  (T::Type{<:CuArray})(::UndefInitializer, sz...) = T(sz...)
-end
-
-if CuArrays.libcudnn != nothing
-  if isdefined(CuArrays, :libcudnn_handle)
-    handle() = CuArrays.libcudnn_handle[]
-  else
-    handle() = CuArrays.CUDNN.handle()
-  end
+if CuArrays.libcudnn !== nothing  # TODO: use CuArrays.has_cudnn()
+  using CuArrays: CUDNN
  include("curnn.jl")
  include("cudnn.jl")
 else
-  @warn("CUDNN is not installed, some functionality will not be available.")
+  @warn "CUDNN is not installed, some functionality will not be available."
 end

 end
--- a/src/cuda/cudnn.jl
+++ b/src/cuda/cudnn.jl
@ -1,228 +1,8 @@
-using .CuArrays.CUDNN: @check, libcudnn, cudnnStatus_t, cudnnTensorDescriptor_t,
-  cudnnBatchNormMode_t, cudnnHandle_t, cudnnDataType, TensorDesc, FilterDesc
 import ..Flux: data
-using LinearAlgebra
+import CuArrays.CUDNN: batchnorm, ∇batchnorm

-mutable struct DropoutDesc
-  ptr::Ptr{Nothing}
-  states::CuVector{UInt8}
-end
+(BN::Flux.BatchNorm)(x::Union{CuArray{T,2},CuArray{T,4},CuArray{T,5}}, cache = nothing) where T<:Union{Float32, Float64} =
+  BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; cache = cache, alpha = 1, beta = 0, eps = BN.ϵ, training = Flux.istraining()))

-Base.unsafe_convert(::Type{Ptr{Nothing}}, dd::DropoutDesc) = dd.ptr
-
-function DropoutDesc(ρ::Real; seed::Integer=0)
-  d = [C_NULL]
-  s = Csize_t[0]
-  @check ccall((:cudnnCreateDropoutDescriptor,libcudnn), cudnnStatus_t, (Ptr{Ptr{Nothing}},), d)
-  @check ccall((:cudnnDropoutGetStatesSize,libcudnn),cudnnStatus_t,(Ptr{Nothing},Ptr{Csize_t}),handle(),s)
-  states = CuArray{UInt8}(undef, s[]) # TODO: can we drop this when ρ=0?
-  desc = DropoutDesc(d[], states)
-  @check ccall((:cudnnSetDropoutDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},Ptr{Nothing},Cfloat,CuPtr{Nothing},Csize_t,Culonglong),
-    desc,handle(),ρ,states,length(states),seed)
-  finalizer(desc) do x
-    @check ccall((:cudnnDestroyDropoutDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x)
-  end
-  return desc
-end
-
-const BATCHNORM_SPATIAL = 1
-const BATCHNORM_ACTIVATION = 0
-const BATCHNORM_MIN_EPS = 1e-5
-
-@inline _wsize(y) = (map(_ -> 1, size(y)[1:end-2])..., size(y)[end-1], 1)
-
-@inline _reddims(y) = (collect(1:ndims(y)-2)..., ndims(y))
-
-mutable struct BNCache
-  mean
-  ivar
-end
-
-BNCache() = BNCache(nothing, nothing)
-
-# NOTE: CuDNN supports only 4D and 5D Tensors for BatchNorm Operations
-# so reshape a 2D Tensor into 4D
-batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T, 2},
-          running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
-          cache = nothing, alpha = T(1), beta = T(0),
-          eps = T(1e-5), training = true) where T<:Union{Float32, Float64} =
-  dropdims(batchnorm(g, b, reshape(x, 1, 1, size(x, 1), size(x, 2)), running_mean, running_var, momentum,
-            cache = cache, alpha = alpha, beta = beta, eps = eps, training = training), dims = (1, 2))
-
-function batchnorm(g::CuArray{T}, b::CuArray{T}, x::Union{CuArray{T, 4},CuArray{T,5}},
-                   running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
-                   cache = nothing, alpha = T(1), beta = T(0),
-                   eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
-  y = similar(x)
-  cudnnBNForward!(y, g, b, x, running_mean, running_var, momentum, cache = cache,
-      alpha = alpha, beta = beta, eps = eps, training = training)
-  y
-end
-
-function cudnnBNForward!(y::CuArray{T}, g::CuArray{T}, b::CuArray{T}, x::CuArray{T},
-                        running_mean::CuArray{T}, running_var::CuArray{T},
-                        momentum; cache = nothing,
-                        alpha = T(1), beta = T(0),
-                        eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
-  dims = _wsize(x)
-  if eps < BATCHNORM_MIN_EPS
-    # warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS)
-    eps = BATCHNORM_MIN_EPS
-  end
-  xd = TensorDesc(x)
-  yd = TensorDesc(y)
-  gd = TensorDesc(T, dims)
-
-  if training
-
-    if cache !== nothing
-      mean = zeros(CuArray{T}, dims...)
-      ivar = ones(CuArray{T}, dims...)
-    else
-      mean = CU_NULL
-      ivar = CU_NULL
-    end
-
-    @check ccall((:cudnnBatchNormalizationForwardTraining, libcudnn), cudnnStatus_t,
-                 (cudnnHandle_t,cudnnBatchNormMode_t,
-                  Ptr{T}, Ptr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T}, CuPtr{T},
-                  Cdouble, CuPtr{T}, CuPtr{T},
-                  Cdouble, CuPtr{T}, CuPtr{T}),
-                  handle(), BATCHNORM_SPATIAL,
-                  Ref(T(alpha)), Ref(T(beta)),
-                  xd, x,
-                  yd, y,
-                  gd, g, b,
-                  momentum, running_mean, running_var,
-                  eps, mean, ivar)
-
-    if cache !== nothing
-      cache.mean = mean
-      cache.ivar = ivar
-    end
-  else
-    @check ccall((:cudnnBatchNormalizationForwardInference, libcudnn), cudnnStatus_t,
-                 (Ptr{cudnnHandle_t},cudnnBatchNormMode_t,
-                  Ptr{T}, Ptr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T}, CuPtr{T},
-                  CuPtr{T}, CuPtr{T},
-                  Cdouble),
-                  handle(), BATCHNORM_SPATIAL,
-                  Ref(T(alpha)), Ref(T(beta)),
-                  xd, x,
-                  yd, y,
-                  gd, g, b,
-                  running_mean, running_var,
-                  eps)
-  end
-end
-
-function ∇batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T, 2}, dy::CuArray{T, 2},
-           running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
-           cache = nothing, eps = T(1e-5), alpha = T(1),
-           beta = T(0), training = true) where T<:Union{Float32, Float64}
-  dg, db, dx = ∇batchnorm(g, b, reshape(x, 1, 1, size(x, 1), size(x, 2)), reshape(dy, 1, 1, size(dy, 1),
-                          size(dy, 2)), running_mean, running_var, momentum, cache = cache, eps = eps,
-                          alpha = alpha, beta = beta, training = training)
-  (dg, db, dropdims(dx, dims = (1, 2)))
-end
-
-function ∇batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T}, dy::CuArray{T},
-                    running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
-                    cache = nothing, eps = T(1e-5), alpha = T(1),
-                    beta = T(0), training = true) where T<:Union{Float32, Float64}
-  dg = similar(g)
-  db = similar(b)
-  dx = similar(x)
-  cudnnBNBackward!(dg, g, db, dx, x, dy, running_mean, running_var, T(momentum),
-    training = training, cache = cache, eps = eps, alpha = alpha, beta = beta)
-  (dg, db, dx)
-end
-
-function cudnnBNBackward!(dg::CuArray{T}, g::CuArray{T}, db::CuArray{T},
-                          dx::CuArray{T}, x::CuArray{T}, dy::CuArray{T},
-                          running_mean::CuArray{T}, running_var::CuArray{T},
-                          momentum; cache = nothing, eps = T(1e-5),
-                          alpha = T(1), beta = T(0),
-                          dalpha = T(1), dbeta = T(0), training = true) where T<:Union{Float32, Float64}
-  if training
-    xd = TensorDesc(x)
-    dyd = TensorDesc(dy)
-    dxd = TensorDesc(dx)
-    gd = TensorDesc(T, _wsize(x))
-    if cache !== nothing
-      mean, ivar = cache.mean, cache.ivar
-      info("mean and ivar are fetched from the cache")
-    else
-      mean, ivar = CU_NULL, CU_NULL
-    end
-
-    if eps < BATCHNORM_MIN_EPS
-      eps = BATCHNORM_MIN_EPS
-    end
-
-    @check ccall((:cudnnBatchNormalizationBackward, libcudnn), cudnnStatus_t,
-                 (cudnnHandle_t,cudnnBatchNormMode_t,
-                  Ptr{T}, Ptr{T},
-                  Ptr{T}, Ptr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T}, CuPtr{T}, CuPtr{T},
-                  Cdouble, CuPtr{T}, CuPtr{T}),
-                  handle(), BATCHNORM_SPATIAL,
-                  Ref(T(alpha)), Ref(T(beta)),
-                  Ref(T(dalpha)), Ref(T(dbeta)),
-                  xd, x,
-                  dyd, dy,
-                  dxd, dx,
-                  gd, g, dg, db,
-                  eps, mean, ivar)
-  else
-    ivar = 1 ./ sqrt.(reshape(running_var, _wsize(x)) .+ eps)
-    dx .= dy .* reshape(g, _wsize(x)) .* ivar
-    dg .= squeeze(sum(dy .* (x .- reshape(running_mean, _wsize(x))) .* ivar, _reddims(dy)), dims = (1,2,4))
-    db .= squeeze(sum(dy, _reddims(dy)), dims = (1,2,4))
-  end
-end
-
-# Flux Interface
-
-(BN::Flux.BatchNorm)(x::Union{CuParam{T,2},CuParam{T,4},CuParam{T,5}}, cache = nothing) where T<:Union{Float32, Float64} =
-  BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; cache = cache, alpha = 1, beta = 0, eps = BN.ϵ, training = BN.active))
-
-batchnorm(g::TrackedArray, b::TrackedArray, x::TrackedArray, running_mean::CuArray{T},
-          running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
-  track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
-
-batchnorm(g::TrackedArray, b::TrackedArray, x::CuArray{T}, running_mean::CuArray{T},
-          running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
-  track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
-
-batchnorm(g::TrackedArray, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T},
-          running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
-  track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
-
-batchnorm(g::CuArray{T}, b::TrackedArray, x::CuArray{T}, running_mean::CuArray{T},
-          running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
-  track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
-
-batchnorm(g::CuArray{T}, b::TrackedArray, x::TrackedArray, running_mean::CuArray{T},
-          running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
-  track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
-
-batchnorm(g::TrackedArray, b::CuArray{T}, x::CuArray{T}, running_mean::CuArray{T},
-          running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
-  track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
-
-batchnorm(g::CuArray{T}, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T},
-          running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
-  track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
-
-@grad batchnorm(g, b, x, running_mean, running_var, momentum; kw...) =
-  batchnorm(data.((g, b, x))..., running_mean, running_var, momentum; kw...), Δ -> (nobacksies(:batchnorm, ∇batchnorm(data.((g, b, x, Δ))..., running_mean, running_var, momentum; kw...))..., nothing, nothing, nothing)
+@adjoint batchnorm(g, b, x, running_mean, running_var, momentum; kw...) =
+  batchnorm(g, b, x, running_mean, running_var, momentum; kw...), Δ -> (∇batchnorm(g, b, x, Δ, running_mean, running_var, momentum; kw...)..., nothing, nothing, nothing)
--- a/src/cuda/curnn.jl
+++ b/src/cuda/curnn.jl
@ -1,325 +1,91 @@
-using .CuArrays.CUDNN: @check, libcudnn, cudnnStatus_t, cudnnTensorDescriptor_t,
-  cudnnBatchNormMode_t, cudnnHandle_t, cudnnDataType, TensorDesc, FilterDesc
-using LinearAlgebra
-
-const RNN_RELU = 0 # Stock RNN with ReLu activation
-const RNN_TANH = 1 # Stock RNN with tanh activation
-const LSTM = 2     # LSTM with no peephole connections
-const GRU = 3      # Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1)
-
-const LINEAR_INPUT = 0
-const SKIP_INPUT = 1
-
-const UNIDIRECTIONAL = 0
-const BIDIRECTIONAL = 1
-
-const RNN_ALGO_STANDARD = 0
-const RNN_ALGO_PERSIST_STATIC = 1
-const RNN_ALGO_PERSIST_DYNAMIC = 2
-
-# param layout:
-# RNN: [weight, bias] × [input, hidden]
-# GRU: [weight, bias] × [input, hidden] × [reset, update, newmem]
-# LSTM: [weight, bias] × [input, hidden] × [input, forget, newmem, output]
-
-function params(w::CuVector, input, hidden, n = 1)
-  slice(offset, shape) = reshape(view(w, offset.+(1:prod(shape))), shape)
-  wx = slice(0, (input, hidden*n))
-  wh = slice(length(wx), (hidden, hidden*n))
-  bias = view(w, length(wx)+length(wh) .+ (1:hidden*n))
-  (wx, wh), bias
-end
-
-mutable struct RNNDesc{T}
-  mode::Int
-  input::Int
-  hidden::Int
-  params::CuVector{T}
-  weights::NTuple{2,CuMatrix{T}}
-  bias::CuVector{T}
-  ptr::Ptr{Nothing}
-end
-
-Base.unsafe_convert(::Type{Ptr{Nothing}}, d::RNNDesc) = d.ptr
-
-function rnnParamSize(T, r, input)
-  size = Csize_t[0]
-  @check ccall((:cudnnGetRNNParamsSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Ptr{Nothing},Ptr{Csize_t},Cint),
-    handle(), r, TensorDesc(T, (1,input,1)), size, cudnnDataType(T))
-  return Int(size[])÷sizeof(T)
-end
-
-ngates(mode) = [1, 1, 4, 3][mode+1]
-ngates(r::RNNDesc) = ngates(r.mode)
-
-function RNNDesc{T}(mode::Int, input::Int, hidden::Int; layers = 1) where T
-  d = [C_NULL]
-  @check ccall((:cudnnCreateRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Ptr{Nothing}},),d)
-
-  dropoutDesc = DropoutDesc(0)
-  inputMode = LINEAR_INPUT
-  direction = UNIDIRECTIONAL
-  algo = RNN_ALGO_STANDARD
-  @check ccall((:cudnnSetRNNDescriptor_v6,libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Cint,Ptr{Nothing},Cint,Cint,Cint,Cint,Cint),
-    handle(),d[],hidden,layers,dropoutDesc,inputMode,direction,mode,algo,cudnnDataType(T))
-
-  w = cuzeros(T, rnnParamSize(T, d[], input))
-  # TODO: avoid reserve allocation here
-  rd = RNNDesc{T}(mode, input, hidden, w, params(w, input, hidden, ngates(mode))..., d[])
-  finalizer(rd) do x
-    @check ccall((:cudnnDestroyRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x)
-  end
-  return rd
-end
-
-function rnnWorkspaceSize(r::RNNDesc, seqlen, xdesc)
-  size = Csize_t[0]
-  @check ccall((:cudnnGetRNNWorkspaceSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Ptr{Ptr{Nothing}},Ptr{Csize_t}),
-    handle(), r, seqlen, xdesc, size)
-  return Int(size[])
-end
-
-const workspace = [CuVector{UInt8}(undef, 1)]
-
-getworkspace(bytes) =
-  length(workspace[]) ≥ bytes ?
-    workspace[] :
-    (workspace[] = CuVector{UInt8}(undef, bytes))
-
-getworkspace(r::RNNDesc, seqlen, xdesc) =
-  getworkspace(rnnWorkspaceSize(r, seqlen, xdesc))
-
-function rnnTrainingReserveSize(r::RNNDesc, seqlen, xdesc)
-  size = Csize_t[0]
-  @check ccall((:cudnnGetRNNTrainingReserveSize,libcudnn), cudnnStatus_t, (Ptr{Nothing}, Ptr{Nothing}, Cint, Ptr{Ptr{Nothing}}, Ptr{Csize_t}),
-    handle(), r, seqlen, xdesc, size)
-  return Int(size[])
-end
-
-function cudnnRNNForward(rnn::RNNDesc{T}, seqlen, xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
-                         workspace, reserve=nothing) where T
-  if reserve == nothing
-    @check ccall((:cudnnRNNForwardInference, libcudnn), cudnnStatus_t,
-                 (Ptr{Nothing}, Ptr{Nothing}, Cint,
-                  Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
-                  Ptr{Nothing}, CuPtr{T},
-                  CuPtr{Nothing}, Csize_t),
-                 handle(), rnn, seqlen,
-                 xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
-                 workspace, length(workspace))
-  else
-    @check ccall((:cudnnRNNForwardTraining, libcudnn), cudnnStatus_t,
-                 (Ptr{Nothing}, Ptr{Nothing}, Cint,
-                  Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
-                  CuPtr{Nothing}, Csize_t, CuPtr{Nothing}, Csize_t),
-                 handle(), rnn, seqlen,
-                 xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
-                 workspace, length(workspace), reserve, length(reserve))
-  end
-end
-
-xDesc(x) = [TensorDesc(eltype(x), (1, size(x, 1), size(x, 2)))]
-
-hDesc(h::Nothing) = C_NULL, CU_NULL
-hDesc(x::Integer) = (@assert x == 0; hDesc(nothing))
-function hDesc(h::CuArray)
-  TensorDesc(eltype(h), (size(h, 1), size(h, 2), 1)), h
-end
-
-# TODO: can we just manipulate strides here?
-# TODO: should use repmat, but this isn't implemented.
-hBatch(x::AbstractVector, h::CuVector) = h
-hBatch(x::AbstractMatrix, h::CuVector) = h .* cuones(1, size(x, 2))
-hBatch(x::AbstractMatrix, h::CuMatrix) = h .* cuones(1, size(h,2) == 1 ? size(x,2) : 1)
-
-function forward(rnn::RNNDesc{T}, x::CuArray{T}, h_::CuArray{T}, c_ = nothing, train = Val{false}) where T
-  h = hBatch(x, h_)
-  c = c_ == nothing ? nothing : hBatch(x, c_)
-  @assert size(x, 1) == rnn.input
-  @assert size(h, 1) == rnn.hidden
-  @assert size(x, 2) == size(h, 2)
-  seqLength = 1
-  xdesc = xDesc(x)
-  y = x isa AbstractVector ? similar(x, rnn.hidden) : similar(x, rnn.hidden, size(x, 2))
-  ho = similar(h)
-  ydesc = xDesc(y)
-  workspace = getworkspace(rnn, seqLength, xdesc)
-  reserve = train == Val{true} ?
-    CuVector{UInt8}(undef, rnnTrainingReserveSize(rnn, seqLength, xdesc)) :
-    nothing
-  co = c == nothing ? c : similar(c)
-  cudnnRNNForward(rnn, seqLength,
-                  xdesc, x,
-                  hDesc(h)...,
-                  hDesc(c)...,
-                  FilterDesc(T, (1, 1, length(rnn.params))), rnn.params,
-                  ydesc, y,
-                  hDesc(ho)...,
-                  hDesc(co)...,
-                  workspace, reserve)
-  result = c == nothing ? (y, ho) : (y, ho, co)
-  return train == Val{true} ? (reserve, result) : result
-end
-
-forwardTrain(rnn::RNNDesc{T}, x::CuArray{T}, h::CuArray{T}, c = nothing) where T =
-  forward(rnn, x, h, c, Val{true})
-
-function cudnnRNNBackwardData(rnn::RNNDesc{T}, seqlen, yd, y, dyd, dy, dhod, dho, dcod, dco,
-                              wd, w, hd, h, cd, c, dxd, dx, dhd, dh, dcd, dc, ws, rs) where T
-  @check ccall((:cudnnRNNBackwardData,libcudnn),cudnnStatus_t,
-               (Ptr{Nothing}, Ptr{Nothing}, Cint,
-                Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
-                Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing},
-                CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
-                CuPtr{Nothing}, Csize_t, CuPtr{Nothing}, Csize_t),
-               handle(), rnn, seqlen, yd, y, dyd, dy, dhod, dho, dcod, dco,
-               wd, w, hd, h, cd, c, dxd, dx, dhd, dh, dcd, dc, ws, length(ws), rs, length(rs))
-end
-
-function backwardData(rnn::RNNDesc{T}, y, dy_, dho, dco, h, c, reserve) where T
-  # Same as above, any more efficient way?
-  dy = dy_ isa Integer ? zero(y) : dy_
-  yd = xDesc(y)
-  dx = y isa AbstractVector ? similar(dy, rnn.input) : similar(dy, rnn.input, size(dy, 2))
-  dh = similar(h)
-  dc = c == nothing ? nothing : similar(c)
-  cudnnRNNBackwardData(rnn, 1,
-    yd, y, yd, dy, hDesc(dho)..., hDesc(dco)...,
-    FilterDesc(T, (1, 1, length(rnn.params))), rnn.params,
-    hDesc(h)..., hDesc(c)..., xDesc(dx), dx, hDesc(dh)..., hDesc(dc)...,
-    workspace[], reserve)
-  return c == nothing ? (dx, dh) : (dx, dh, dc)
-end
-
-backwardData(rnn, y, dy, dho, hx, reserve) =
-  backwardData(rnn, y, dy, dho, nothing, hx, nothing, reserve)
-
-function cudnnRNNBackwardWeights(rnn::RNNDesc{T}, seqlen, xd, x, hd, h, yd, y, dwd, dw,
-                                 workspace, reserve) where T
-  @check ccall((:cudnnRNNBackwardWeights,libcudnn), cudnnStatus_t,
-               (Ptr{Nothing}, Ptr{Nothing}, Cint,  # handle, rnnDesc, seqLength
-                Ptr{Ptr{Nothing}}, CuPtr{T}, #x
-                Ptr{Nothing}, CuPtr{T}, #hx
-                Ptr{Ptr{Nothing}}, CuPtr{T}, #y
-                CuPtr{Nothing}, Csize_t, #ws
-                Ptr{Nothing}, CuPtr{T}, #dw
-                CuPtr{Nothing}, Csize_t), #rs
-               handle(), rnn, seqlen, xd, x, hd, h, yd, y,
-               workspace, length(workspace), dwd, dw, reserve, length(reserve))
-end
-
-function backwardWeights(rnn::RNNDesc{T}, x, h, y, reserve) where T
-  dw = zero(rnn.params)
-  cudnnRNNBackwardWeights(rnn, 1,
-    xDesc(x), x, hDesc(h)..., xDesc(y), y,
-    FilterDesc(T, (1, 1, length(dw))), dw,
-    workspace[], reserve)
-  return params(dw, rnn.input, rnn.hidden, ngates(rnn))
-end
-
-# Interface
-
 import ..Flux: Flux, relu
-import ..Tracker: TrackedArray
-using .CuArrays.CUDAnative
-using .CuArrays: @cuindex, cudims
+using CuArrays.CUDAnative
+using CuArrays: @cuindex, cudims

-function LinearAlgebra.copy_transpose!(dst::CuArray, src::CuArray)
-  function kernel(dst, src)
-    I = @cuindex dst
-    dst[I...] = src[reverse(I)...]
-    return
-  end
-  blk, thr = cudims(dst)
-  @cuda blocks=blk threads=thr kernel(dst, src)
-  return dst
-end
-
-CuParam{T,N} = Union{CuArray{T,N},TrackedArray{T,N,CuArray{T,N}}}
-CuRNN{T} = Flux.RNNCell{<:Union{typeof(tanh),typeof(relu)},<:CuParam{T,2},<:CuParam{T,1}}
-CuGRU{T} = Flux.GRUCell{<:CuParam{T,2},<:CuParam{T,1}}
-CuLSTM{T} = Flux.LSTMCell{<:CuParam{T,2},<:CuParam{T,1}}
+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}}
+CuLSTM{T} = Flux.LSTMCell{<:CuArray{T,2},<:CuArray{T,1}}
 CuRNNs{T} = Union{CuRNN{T},CuGRU{T},CuLSTM{T}}

-function copyparams!(m::CuRNNs, d::RNNDesc)
-  Wi, Wh = d.weights
-  copy_transpose!(Wi, Flux.data(m.Wi))
-  copy_transpose!(Wh, Flux.data(m.Wh))
-  copy_transpose!(d.bias, Flux.data(m.b))
-  return
-end
-
-function RNNDesc(m::CuRNNs{T}) where T
+function CUDNN.RNNDesc(m::CuRNNs{T}) where T
  h, i = length(m.h), size(m.Wi, 2)
  mode = m isa CuRNN ?
-    (m.σ == tanh ? RNN_TANH : RNN_RELU) :
-    m isa CuGRU ? GRU : LSTM
-  r = RNNDesc{T}(mode, i, h)
+    (m.σ == tanh ? CUDNN.CUDNN_RNN_TANH : CUDNN.CUDNN_RNN_RELU) :
+    m isa CuGRU ? CUDNN.CUDNN_GRU : CUDNN.CUDNN_LSTM
+  r = CUDNN.RNNDesc{T}(mode, i, h)
  return r
 end

 const descs = WeakKeyDict()

 function desc(rnn)
-  d = haskey(descs, rnn) ? descs[rnn] : (descs[rnn] = RNNDesc(rnn))
-  copyparams!(rnn, d)
+  d = haskey(descs, rnn) ? descs[rnn] : (descs[rnn] = CUDNN.RNNDesc(rnn))
+  CUDNN.setweights!(d, rnn.Wi, rnn.Wh, rnn.b)
  return d
 end

-import Flux.Tracker
-import Flux.Tracker: data, istracked, track, unbroadcast, @grad, nobacksies
+import Zygote
+using Zygote: @adjoint

-istrain(m::CuRNNs, args...) = any(x -> x isa TrackedArray, (m.Wi, m.Wh, m.b, args...))
-
-function (m::CuRNN{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64}
-  result = istrain(m, h, x) ?
-    track(m, x, h, m.Wi, m.Wh, m.b) :
-    forward(desc(m), x, h)
-  return result[2], result[1]
+function (m::CuRNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
+  y, h′ = CUDNN.forward(desc(m), x, h)
+  return h′, y
 end

-function (m::CuGRU{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64}
-  result = istrain(m, h, x) ?
-    track(m, x, h, m.Wi, m.Wh, m.b) :
-    forward(desc(m), x, h)
-  return result[2], result[1]
+function (m::CuGRU{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
+  y, h′ = CUDNN.forward(desc(m), x, h)
+  return h′, y
 end

-function (m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x::CuParam{T}) where T <: Union{Float32,Float64}
-  result = istrain(m, h, x) ?
-    track(m, x, h[1], h[2], m.Wi, m.Wh, m.b) :
-    forward(desc(m), x, h[1], h[2])
-  return (result[2], result[3]), result[1]
+function (m::CuLSTM{T})(h::NTuple{2,CuArray{T}}, x::CuArray{T}) where T <: Union{Float32,Float64}
+  y, h′, c′ = CUDNN.forward(desc(m), x, h[1], h[2])
+  return (h′, c′), y
 end

-(m::CuRNN{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
-(m::CuGRU{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
-(m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
+(m::CuRNN{T})(h::CuArray{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
+(m::CuGRU{T})(h::CuArray{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
+(m::CuLSTM{T})(h::NTuple{2,CuArray{T}}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))

-@grad function (m::Union{CuRNN,CuGRU})(x, h, Wi, Wh, b)
-  reserve, result = forwardTrain(desc(m), data(x), data(h))
-  result, function (Δ)
-    y, ho = result
-    dy, dho = Δ
-    h_ = hBatch(x, data(h))
-    dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve)
-    (dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve)
-    nobacksies(:RNN, (dx, unbroadcast(h, dh), transpose(dWi), transpose(dWh), db))
+trim(x, Δ) = reshape(Δ, ntuple(i -> size(Δ, i), Val(ndims(x))))
+
+unbroadcast(x::AbstractArray, Δ) =
+  size(x) == size(Δ) ? Δ :
+  length(x) == length(Δ) ? trim(x, Δ) :
+    trim(x, sum(Δ, dims = ntuple(i -> size(x, i) == 1 ? i : ndims(Δ)+1, Val(ndims(Δ)))))
+
+coerce_cuda(x::Union{CuArray,Nothing}) = x
+coerce_cuda(x::Tuple) = coerce_cuda.(x)
+
+coerce_cuda(x::AbstractArray) = x .+ CuArrays.fill(0)
+
+function struct_grad!(cx::Zygote.Context, x, x̄)
+  for f in fieldnames(typeof(x))
+    Zygote.accum_param(cx, getfield(x, f), getfield(x̄, f))
+  end
+  dx = Zygote.grad_mut(cx, x)
+  dx[] = Zygote.accum(dx[], x̄)
+  return dx
+end
+
+for RNN in (CuRNN, CuGRU)
+  @eval @adjoint function (m::$RNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
+    (y, ho), back = CUDNN.pullback(desc(m), x, h)
+    (ho, y), function (Δ)
+      dho, dy = coerce_cuda(Δ) # Support FillArrays etc.
+      m̄ = back(dy, dho)
+      dm = struct_grad!(__context__, m, (σ=nothing,Wi=transpose(m̄.Wi),Wh=transpose(m̄.Wh),b=m̄.b,h=nothing))
+      (dm, unbroadcast(h, m̄.h), m̄.x)
+    end
  end
 end

-@grad function (m::CuLSTM)(x, h, c, Wi, Wh, b)
-  reserve, result = forwardTrain(desc(m), data.((x, h, c))...)
-  result, function (Δ)
-    y, ho = result
-    dy, dho, dco = Δ
-    h_ = hBatch(x, data(h))
-    c_ = hBatch(x, data(c))
-    dx, dh, dc = backwardData(descs[m], y, dy, dho, dco, h_, c_, reserve)
-    (dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve)
-    nobacksies(:RNN,
-      (dx, unbroadcast(h, dh), unbroadcast(c, dc),
-       transpose(dWi), transpose(dWh), db))
+@adjoint function (m::CuLSTM)((h, c)::Tuple{CuArray{T},CuArray{T}}, x::CuArray{T}) where T <: Union{Float32,Float64}
+  (y, ho, co), back = CUDNN.pullback(desc(m), x, h, c)
+  ((ho, co), y), function (Δ)
+    dhc, dy = coerce_cuda(Δ) # Support FillArrays etc.
+    dho, dco = dhc === nothing ? (nothing, nothing) : dhc
+    m̄ = back(dy, dho, dco)
+    dm = struct_grad!(__context__, m, (σ=nothing,Wi=transpose(m̄.Wi),Wh=transpose(m̄.Wh),b=m̄.b,h=nothing,c=nothing))
+    (dm, (unbroadcast(h, m̄.h), unbroadcast(c, m̄.c)), m̄.x)
  end
 end
--- a/src/data/iris.jl
+++ b/src/data/iris.jl
@ -1,14 +1,10 @@
-
 """
-
-    Iris
-
 Fisher's classic iris dataset.

-Measurements from 3 different species of iris: setosa, versicolor and 
+Measurements from 3 different species of iris: setosa, versicolor and
 virginica.  There are 50 examples of each species.

-There are 4 measurements for each example: sepal length, sepal width, petal 
+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).
@ -35,10 +31,12 @@ end

    labels()

-Get the labels of the iris dataset, a 150 element array of strings listing the 
+Get the labels of the iris dataset, a 150 element array of strings listing the
 species of each example.

 ```jldoctest
+julia> using Flux
+
 julia> labels = Flux.Data.Iris.labels();

 julia> summary(labels)
@ -58,11 +56,13 @@ end

    features()

-Get the features of the iris dataset.  This is a 4x150 matrix of Float64 
-elements.  It has a row for each feature (sepal length, sepal width, 
+Get the features of the iris dataset.  This is a 4x150 matrix of Float64
+elements.  It has a row for each feature (sepal length, sepal width,
 petal length, petal width) and a column for each example.

 ```jldoctest
+julia> using Flux
+
 julia> features = Flux.Data.Iris.features();

 julia> summary(features)
@ -81,6 +81,5 @@ function features()
    iris = readdlm(deps("iris.data"), ',')
    Matrix{Float64}(iris[1:end, 1:4]')
 end
+
 end
-
-
--- a/src/deprecations.jl
+++ b/src/deprecations.jl
@ -0,0 +1,2 @@
+@deprecate param(x) x
+@deprecate data(x) x
--- a/src/functor.jl
+++ b/src/functor.jl
@ -0,0 +1,91 @@
+import Adapt: adapt, adapt_storage
+using Zygote: IdSet
+
+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]
+
+params!(p::Params, x::AbstractArray{<:Real}, seen = IdSet()) = push!(p, x)
+
+function params!(p::Params, x, seen = IdSet())
+  x in seen && return
+  push!(seen, x)
+  for child in trainable(x)
+    params!(p, child, seen)
+  end
+end
+
+function params(m...)
+  ps = Params()
+  params!(ps, m)
+  return ps
+end
+
+# Deprecated stuff
+macro treelike(args...)
+  functorm(args...)
+end
+mapleaves(f, x) = fmap(f, x)
+
+function loadparams!(m, xs)
+  for (p, x) in zip(params(m), xs)
+    size(p) == size(x) ||
+      error("Expected param size $(size(p)), got $(size(x))")
+    copyto!(p, x)
+  end
+end
+
+# CPU/GPU movement conveniences
+
+cpu(m) = fmap(x -> adapt(Array, x), m)
+
+const gpu_adaptor = if has_cuarrays()
+  CuArrays.cu
+else
+  identity
+end
+
+gpu(x) = fmap(gpu_adaptor, x)
+
+# Precision
+
+adapt_storage(T::Type{<:Real}, xs::AbstractArray{<:Real}) = convert.(T, xs)
+
+paramtype(T::Type{<:Real}, m) = fmap(x -> adapt(T, x), m)
+
+f32(m) = paramtype(Float32, m)
+f64(m) = paramtype(Float64, m)
--- a/src/layers/basic.jl
+++ b/src/layers/basic.jl
@ -24,8 +24,7 @@ end
@forward Chain.layers Base.getindex, Base.length, Base.first, Base.last,
  Base.iterate, Base.lastindex

-children(c::Chain) = c.layers
-mapchildren(f, c::Chain) = Chain(f.(c.layers)...)
+functor(c::Chain) = c.layers, ls -> Chain(ls...)

 applychain(::Tuple{}, x) = x
 applychain(fs::Tuple, x) = applychain(tail(fs), first(fs)(x))
@ -89,10 +88,10 @@ Dense(W, b) = Dense(W, b, identity)

 function Dense(in::Integer, out::Integer, σ = identity;
               initW = glorot_uniform, initb = zeros)
-  return Dense(param(initW(out, in)), param(initb(out)), σ)
+  return Dense(initW(out, in), initb(out), σ)
 end

-@treelike Dense
+@functor Dense

 function (a::Dense)(x::AbstractArray)
  W, b, σ = a.W, a.b, a.σ
@ -110,7 +109,7 @@ end
 (a::Dense{<:Any,W})(x::AbstractArray{T}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
  invoke(a, Tuple{AbstractArray}, x)

-(a::Dense{<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
+(a::Dense{<:Any,W})(x::AbstractArray{<:AbstractFloat}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
  a(T.(x))

 """
@ -129,9 +128,9 @@ struct Diagonal{T}
 end

 Diagonal(in::Integer; initα = ones, initβ = zeros) =
-  Diagonal(param(initα(in)), param(initβ(in)))
+  Diagonal(initα(in), initβ(in))

-@treelike Diagonal
+@functor Diagonal

 function (a::Diagonal)(x)
  α, β = a.α, a.β
@ -184,41 +183,42 @@ function Maxout(f, n_alts)
  return Maxout(over)
 end

-@treelike Maxout
+@functor Maxout

 function (mo::Maxout)(input::AbstractArray)
    mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over)
 end

 """
-    SkipConnection(layers...)
+    SkipConnection(layers, connection)

-Creates a Skip Connection, which constitutes of a layer or Chain of consecutive layers
-and a shortcut connection linking the input to the block to the
-output through a user-supplied callable.
+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.

-`SkipConnection` requires the output dimension to be the same as the input.
+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:
+```
+m = Conv((3,3), 4=>7, pad=(1,1))
+x = ones(5,5,4,10);
+size(m(x)) == (5, 5, 7, 10)

-A 'ResNet'-type skip-connection with identity shortcut would simply be
-```julia
-    SkipConnection(layer, (a,b) -> a + b)
+sm = SkipConnection(m, (mx, x) -> cat(mx, x, dims=3))
+size(sm(x)) == (5, 5, 11, 10)
 ```
 """
-
 struct SkipConnection
  layers
  connection  #user can pass arbitrary connections here, such as (a,b) -> a + b
 end

-@treelike SkipConnection
+@functor SkipConnection

 function (skip::SkipConnection)(input)
-  #We apply the layers to the input and return the result of the application of the layers and the original input
  skip.connection(skip.layers(input), input)
 end

 function Base.show(io::IO, b::SkipConnection)
-  print(io, "SkipConnection(")
-  join(io, b.layers, ", ")
-  print(io, ")")
+  print(io, "SkipConnection(", b.layers, ", ", b.connection, ")")
 end
--- a/src/layers/conv.jl
+++ b/src/layers/conv.jl
@ -14,11 +14,11 @@ Example: Applying Conv layer to a 1-channel input using a 2x2 window size,

    size = (2,2)
    in = 1
-    out = 16 
+    out = 16
    Conv((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, 
+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.

 Takes the keyword arguments `pad`, `stride` and `dilation`.
@ -42,10 +42,10 @@ end

 Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
     init = glorot_uniform,  stride = 1, pad = 0, dilation = 1) where N =
-  Conv(param(init(k..., ch...)), param(zeros(ch[2])), σ,
+  Conv(init(k..., ch...), zeros(ch[2]), σ,
       stride = stride, pad = pad, dilation = dilation)

-@treelike Conv
+@functor Conv

 function (c::Conv)(x::AbstractArray)
  # TODO: breaks gpu broadcast :(
@ -74,8 +74,10 @@ end

 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. 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`.
 """
 struct ConvTranspose{N,M,F,A,V}
@ -97,10 +99,10 @@ end

 ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
              init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N =
-ConvTranspose(param(init(k..., reverse(ch)...)), param(zeros(ch[2])), σ,
+ConvTranspose(init(k..., reverse(ch)...), zeros(ch[2]), σ,
              stride = stride, pad = pad, dilation = dilation)

-@treelike ConvTranspose
+@functor ConvTranspose

 function conv_transpose_dims(c::ConvTranspose, x::AbstractArray)
    # Calculate size of "input", from ∇conv_data()'s perspective...
@ -169,8 +171,8 @@ function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ =
     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(
-    param(init(k..., div(ch[2], ch[1]), ch[1])),
-    param(zeros(ch[2])),
+    init(k..., div(ch[2], ch[1]), ch[1]),
+    zeros(ch[2]),
    σ;
    stride = stride,
    pad = pad,
@ -178,7 +180,7 @@ function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ =
  )
 end

-@treelike DepthwiseConv
+@functor DepthwiseConv

 function (c::DepthwiseConv)(x)
  σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
@ -198,25 +200,26 @@ end

 (a::DepthwiseConv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
  a(T.(x))
+
 """
    CrossCor(size, in=>out)
    CrossCor(size, in=>out, relu)
-  
+
 Standard cross convolutional layer. `size` should be a tuple like `(2, 2)`.
 `in` and `out` specify the number of input and output channels respectively.
-    
+
 Example: Applying CrossCor layer to a 1-channel input using a 2x2 window size,
         giving us a 16-channel output. Output is activated with ReLU.
-    
+
    size = (2,2)
    in = 1
-    out = 16 
+    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, 
+
+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.
-    
+
 Takes the keyword arguments `pad`, `stride` and `dilation`.
 """
 struct CrossCor{N,M,F,A,V}
@ -238,10 +241,10 @@ end

 CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
     init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N =
-  CrossCor(param(init(k..., ch...)), param(zeros(ch[2])), σ,
+  CrossCor(init(k..., ch...), zeros(ch[2]), σ,
       stride = stride, pad = pad, dilation = dilation)

-@treelike CrossCor
+@functor CrossCor

 function crosscor(x, w, ddims::DenseConvDims)
  ddims = DenseConvDims(ddims, F=true)
--- a/src/layers/normalise.jl
+++ b/src/layers/normalise.jl
@ -1,17 +1,20 @@
-"""
-    testmode!(m)
-    testmode!(m, false)
+istraining() = false

-Put layers like [`Dropout`](@ref) and [`BatchNorm`](@ref) into testing mode
-(or back to training mode with `false`).
-"""
-function testmode!(m, val::Bool=true)
-  prefor(x -> _testmode!(x, val), m)
-  return m
+@adjoint istraining() = true, _ -> nothing
+
+_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 = :) = x
+
+@adjoint function dropout(x, p; dims = :)
+  y = rand!(similar(x, _dropout_shape(x, dims)))
+  y .= _dropout_kernel.(y, p, 1 - p)
+  return x .* y, Δ -> (Δ .* y, nothing)
 end

-_testmode!(m, test) = nothing
-
 """
    Dropout(p, dims = :)

@ -19,79 +22,52 @@ 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).
-
-Does nothing to the input once in [`testmode!`](@ref).
 """
-mutable struct Dropout{F}
+mutable struct Dropout{F,D}
  p::F
-  dims::Union{Colon, Int, NTuple{N, Int} where N}
-  active::Bool
+  dims::D
 end

 function Dropout(p; dims = :)
  @assert 0 ≤ p ≤ 1
-  Dropout{typeof(p)}(p, dims, true)
+  Dropout{typeof(p),typeof(dims)}(p, dims)
 end

-_dropout_shape(s, ::Colon) = size(s)
-_dropout_shape(s, dims) = tuple((i ∉ dims ? 1 : si for (i, si) ∈ enumerate(size(s)))...)
+(a::Dropout)(x) = dropout(x, a.p; dims = a.dims)

-_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)`. 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.
-"""
-function dropout(x, p; dims = :)
-  y = similar(x, _dropout_shape(x, dims))
-  rand!(y)
-  y .= _dropout_kernel.(y, p, 1 - p)
-  return x .* y
+function Base.show(io::IO, d::Dropout)
+  print(io, "Dropout(", d.p)
+  d.dims != (:) && print(io, ", dims = $(repr(d.dims))")
+  print(io, ")")
 end

-function (a::Dropout)(x)
-  a.active || return x
-  return dropout(x, a.p; dims = a.dims)
-end
-
-_testmode!(a::Dropout, test) = (a.active = !test)
-
 """
    AlphaDropout(p)
-A dropout layer. It is used in Self-Normalizing Neural Networks. 
+A dropout layer. It is used in Self-Normalizing Neural Networks.
 (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf)
 The AlphaDropout layer ensures that mean and variance of activations remains the same as before.
 """
 mutable struct AlphaDropout{F}
  p::F
-  active::Bool
-end
-
-function AlphaDropout(p)
-  @assert 0 ≤ p ≤ 1
-  AlphaDropout(p,true)
+  function AlphaDropout(p)
+    @assert 0 ≤ p ≤ 1
+    new{typeof(p)}(p)
+  end
 end

 function (a::AlphaDropout)(x)
-  a.active || return x
+  istraining() || return x
  λ = eltype(x)(1.0507009873554804934193349852946)
  α = eltype(x)(1.6732632423543772848170429916717)
  α1 = eltype(x)(-λ*α)
  noise = randn(eltype(x), size(x))
-  x = @. x*(noise > (1 - a.p)) + α1 * (noise <= (1 - a.p))
+  x = @. x*(noise > (1 - a.p)) + α1 * (noise < (1 - a.p))
  A = (a.p + a.p * (1 - a.p) * α1 ^ 2)^0.5
  B = -A * α1 * (1 - a.p)
  x = @. A * x + B
  return x
 end

-_testmode!(a::AlphaDropout, test) = (a.active = !test)
-
 """
    LayerNorm(h::Integer)

@ -106,7 +82,7 @@ end
 LayerNorm(h::Integer) =
  LayerNorm(Diagonal(h))

-@treelike LayerNorm
+@functor LayerNorm

 (a::LayerNorm)(x) = a.diag(normalise(x))

@ -151,25 +127,25 @@ mutable struct BatchNorm{F,V,W,N}
  σ²::W  # moving std
  ϵ::N
  momentum::N
-  active::Bool
 end

 BatchNorm(chs::Integer, λ = identity;
          initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
-  BatchNorm(λ, param(initβ(chs)), param(initγ(chs)),
-            zeros(chs), ones(chs), ϵ, momentum, true)
+  BatchNorm(λ, initβ(chs), initγ(chs),
+            zeros(chs), ones(chs), ϵ, momentum)
+
+trainable(bn::BatchNorm) = (bn.β, bn.γ)

 function (BN::BatchNorm)(x)
  size(x, ndims(x)-1) == length(BN.β) ||
    error("BatchNorm expected $(length(BN.β)) channels, got $(size(x, ndims(x)-1))")
  dims = length(size(x))
  channels = size(x, dims-1)
-  affine_shape = ones(Int, dims)
-  affine_shape[end-1] = channels
-  m = prod(size(x)[1:end-2]) * size(x)[end]
+  affine_shape = ntuple(i->i == ndims(x) - 1 ? size(x, i) : 1, ndims(x))
+  m = div(prod(size(x)), channels)
  γ = reshape(BN.γ, affine_shape...)
  β = reshape(BN.β, affine_shape...)
-  if !BN.active
+  if !istraining()
    μ = reshape(BN.μ, affine_shape...)
    σ² = reshape(BN.σ², affine_shape...)
    ϵ = BN.ϵ
@ -178,11 +154,12 @@ function (BN::BatchNorm)(x)
    axes = [1:dims-2; dims] # axes to reduce along (all but channels axis)
    μ = mean(x, dims = axes)
    σ² = sum((x .- μ) .^ 2, dims = axes) ./ m
-    ϵ = data(convert(T, BN.ϵ))
+    ϵ = convert(T, BN.ϵ)
    # update moving mean/std
-    mtm = data(convert(T, BN.momentum))
-    BN.μ = (1 - mtm) .* BN.μ .+ mtm .* reshape(data(μ), :)
-    BN.σ² = (1 - mtm) .* BN.σ² .+ (mtm * m / (m - 1)) .* reshape(data(σ²), :)
+    mtm = BN.momentum
+    S = eltype(BN.μ)
+    BN.μ  = (1 - mtm) .* BN.μ .+ mtm .* S.(reshape(μ, :))
+    BN.σ² = (1 - mtm) .* BN.σ² .+ (mtm * m / (m - 1)) .* S.(reshape(σ², :))
  end

  let λ = BN.λ
@ -191,13 +168,7 @@ function (BN::BatchNorm)(x)
  end
 end

-children(BN::BatchNorm) =
-  (BN.λ, BN.β, BN.γ, BN.μ, BN.σ², BN.ϵ, BN.momentum, BN.active)
-
-mapchildren(f, BN::BatchNorm) =  # e.g. mapchildren(cu, BN)
-  BatchNorm(BN.λ, f(BN.β), f(BN.γ), f(BN.μ), f(BN.σ²), BN.ϵ, BN.momentum, BN.active)
-
-_testmode!(BN::BatchNorm, test) = (BN.active = !test)
+@functor BatchNorm

 function Base.show(io::IO, l::BatchNorm)
  print(io, "BatchNorm($(join(size(l.β), ", "))")
@ -244,13 +215,14 @@ mutable struct InstanceNorm{F,V,W,N}
  σ²::W  # moving std
  ϵ::N
  momentum::N
-  active::Bool
 end

 InstanceNorm(chs::Integer, λ = identity;
          initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) =
-  InstanceNorm(λ, param(initβ(chs)), param(initγ(chs)),
-            zeros(chs), ones(chs), ϵ, momentum, true)
+  InstanceNorm(λ, initβ(chs), initγ(chs),
+            zeros(chs), ones(chs), ϵ, momentum)
+
+trainable(in::InstanceNorm) = (in.β, in.γ)

 function (in::InstanceNorm)(x)
  size(x, ndims(x)-1) == length(in.β) ||
@ -261,28 +233,26 @@ function (in::InstanceNorm)(x)
  dims = length(size(x))
  c = size(x, dims-1)
  bs = size(x, dims)
-  affine_shape = ones(Int, dims)
-  affine_shape[end-1] = c
-  affine_shape[end] = bs
-  m = prod(size(x)[1:end-2])
+  affine_shape = ntuple(i->i == ndims(x) - 1 || i == ndims(x) ? size(x, i) : 1, ndims(x))
+  m = div(prod(size(x)), c*bs)
  γ, β = expand_inst(in.γ, affine_shape), expand_inst(in.β, affine_shape)

-  if !in.active
+  if !istraining()
    μ = expand_inst(in.μ, affine_shape)
    σ² = expand_inst(in.σ², affine_shape)
    ϵ = in.ϵ
  else
    T = eltype(x)

-    ϵ = data(convert(T, in.ϵ))
+    ϵ = convert(T, in.ϵ)
    axes = 1:dims-2 # axes to reduce along (all but channels and batch size axes)
    μ = mean(x, dims = axes)
    σ² = mean((x .- μ) .^ 2, dims = axes)
-
+    S = eltype(in.μ)
    # update moving mean/std
-    mtm = data(convert(T, in.momentum))
-    in.μ = dropdims(mean(repeat((1 - mtm) .* in.μ, outer=[1, bs]) .+ mtm .* reshape(data(μ), (c, bs)), dims = 2), dims=2)
-    in.σ² = dropdims(mean((repeat((1 - mtm) .* in.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (c, bs))), dims = 2), dims=2)
+    mtm = in.momentum
+    in.μ = dropdims(mean(repeat((1 - mtm) .* in.μ, outer=[1, bs]) .+ mtm .* S.(reshape(μ, (c, bs))), dims = 2), dims=2)
+    in.σ² = dropdims(mean((repeat((1 - mtm) .* in.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* S.(reshape(σ², (c, bs)))), dims = 2), dims=2)
  end

  let λ = in.λ
@ -291,13 +261,7 @@ function (in::InstanceNorm)(x)
  end
 end

-children(in::InstanceNorm) =
-  (in.λ, in.β, in.γ, in.μ, in.σ², in.ϵ, in.momentum, in.active)
-
-mapchildren(f, in::InstanceNorm) =  # e.g. mapchildren(cu, in)
-  InstanceNorm(in.λ, f(in.β), f(in.γ), f(in.μ), f(in.σ²), in.ϵ, in.momentum, in.active)
-
-_testmode!(in::InstanceNorm, test) = (in.active = !test)
+@functor InstanceNorm

 function Base.show(io::IO, l::InstanceNorm)
  print(io, "InstanceNorm($(join(size(l.β), ", "))")
@ -306,11 +270,11 @@ function Base.show(io::IO, l::InstanceNorm)
 end

 """
-Group Normalization. 
+Group Normalization.
 This layer can outperform Batch-Normalization and Instance-Normalization.

 	GroupNorm(chs::Integer, G::Integer, λ = identity;
-	          initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), 
+	          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.
@ -322,12 +286,11 @@ The number of channels must be an integer multiple of the number of groups.
 Example:
 ```
 m = Chain(Conv((3,3), 1=>32, leakyrelu;pad = 1),
-          GroupNorm(32,16)) # 32 channels, 16 groups (G = 16), thus 2 channels per group used          
+          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
  λ::F  # activation function
@ -337,13 +300,14 @@ mutable struct GroupNorm{F,V,W,N,T}
  σ²::W  # moving std
  ϵ::N
  momentum::N
-  active::Bool
 end

 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, param(initβ(chs)), param(initγ(chs)),
-            zeros(G,1), ones(G,1), ϵ, momentum, true)
+  GroupNorm(G, λ, initβ(chs), initγ(chs),
+            zeros(G,1), ones(G,1), ϵ, momentum)
+
+trainable(gn::GroupNorm) = (gn.β, gn.γ)

 function(gn::GroupNorm)(x)
  size(x,ndims(x)-1) == length(gn.β) || error("Group Norm expected $(length(gn.β)) channels, but got $(size(x,ndims(x)-1)) channels")
@ -355,20 +319,17 @@ function(gn::GroupNorm)(x)
  channels = size(x, dims-1)
  batches = size(x,dims)
  channels_per_group = div(channels,groups)
-  affine_shape = ones(Int, dims)
+  affine_shape = ntuple(i->i == ndims(x) - 1 ? size(x, i) : 1, ndims(x))

  # Output reshaped to (W,H...,C/G,G,N)
-  affine_shape[end-1] = channels
-
-  μ_affine_shape = ones(Int,dims + 1)
-  μ_affine_shape[end-1] = groups
+  μ_affine_shape = ntuple(i->i == ndims(x) ? groups : 1, ndims(x) + 1)

  m = prod(size(x)[1:end-2]) * channels_per_group
  γ = reshape(gn.γ, affine_shape...)
  β = reshape(gn.β, affine_shape...)
-  
+
  y = reshape(x,((size(x))[1:end-2]...,channels_per_group,groups,batches))
-  if !gn.active
+  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)
@ -379,31 +340,25 @@ function(gn::GroupNorm)(x)
    axes = [(1:ndims(y)-2)...] # axes to reduce along (all but channels axis)
    μ = mean(y, dims = axes)
    σ² = mean((y .- μ) .^ 2, dims = axes)
-    
-    ϵ = data(convert(T, gn.ϵ))
-    # update moving mean/std
-    mtm = data(convert(T, gn.momentum))

-    gn.μ = mean((1 - mtm) .* gn.μ .+ mtm .* reshape(data(μ), (groups,batches)),dims=2)
-    gn.σ² = mean((1 - mtm) .* gn.σ² .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (groups,batches)),dims=2)
+    ϵ = convert(T, gn.ϵ)
+    # update moving mean/std
+    mtm = gn.momentum
+    S = eltype(gn.μ)
+    gn.μ = mean((1 - mtm) .* gn.μ .+ mtm .* S.(reshape(μ, (groups,batches))),dims=2)
+    gn.σ² = mean((1 - mtm) .* gn.σ² .+ (mtm * m / (m - 1)) .* S.(reshape(σ², (groups,batches))),dims=2)
  end

  let λ = gn.λ
    x̂ = (y .- μ) ./ sqrt.(σ² .+ ϵ)

-    # Reshape x̂  
+    # Reshape x̂
    x̂ = reshape(x̂,og_shape)
    λ.(γ .* x̂ .+ β)
  end
 end

-children(gn::GroupNorm) =
-  (gn.λ, gn.β, gn.γ, gn.μ, gn.σ², gn.ϵ, gn.momentum, gn.active)
-
-mapchildren(f, gn::GroupNorm) =  # e.g. mapchildren(cu, BN)
-  GroupNorm(gn.G,gn.λ, f(gn.β), f(gn.γ), f(gn.μ), f(gn.σ²), gn.ϵ, gn.momentum, gn.active)
-
-_testmode!(gn::GroupNorm, test) = (gn.active = !test)
+@functor GroupNorm

 function Base.show(io::IO, l::GroupNorm)
  print(io, "GroupNorm($(join(size(l.β), ", "))")
--- a/src/layers/recurrent.jl
+++ b/src/layers/recurrent.jl
@ -38,25 +38,10 @@ function (m::Recur)(xs...)
  return y
 end

-@treelike Recur cell, init
+@functor Recur cell, init

 Base.show(io::IO, m::Recur) = print(io, "Recur(", m.cell, ")")

-_truncate(x::AbstractArray) = Tracker.data(x)
-_truncate(x::Tuple) = _truncate.(x)
-
-"""
-    truncate!(rnn)
-
-Truncates the gradient of the hidden state in recurrent layers. The value of the
-state is preserved. See also `reset!`.
-
-Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to
-
-    rnn.state = Tracker.data(rnn.state)
-"""
-truncate!(m) = prefor(x -> x isa Recur && (x.state = _truncate(x.state)), m)
-
 """
    reset!(rnn)

@ -67,7 +52,8 @@ Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to

    rnn.state = hidden(rnn.cell)
 """
-reset!(m) = prefor(x -> x isa Recur && (x.state = x.init), m)
+reset!(m::Recur) = (m.state = m.init)
+reset!(m) = foreach(reset!, functor(m)[1])

 flip(f, xs) = reverse(f.(reverse(xs)))

@ -83,8 +69,8 @@ end

 RNNCell(in::Integer, out::Integer, σ = tanh;
        init = glorot_uniform) =
-  RNNCell(σ, param(init(out, in)), param(init(out, out)),
-          param(init(out)), param(zeros(out)))
+  RNNCell(σ, init(out, in), init(out, out),
+          init(out), zeros(out))

 function (m::RNNCell)(h, x)
  σ, Wi, Wh, b = m.σ, m.Wi, m.Wh, m.b
@ -94,7 +80,7 @@ end

 hidden(m::RNNCell) = m.h

-@treelike RNNCell
+@functor RNNCell

 function Base.show(io::IO, l::RNNCell)
  print(io, "RNNCell(", size(l.Wi, 2), ", ", size(l.Wi, 1))
@ -122,9 +108,9 @@ end

 function LSTMCell(in::Integer, out::Integer;
                  init = glorot_uniform)
-  cell = LSTMCell(param(init(out*4, in)), param(init(out*4, out)), param(init(out*4)),
-                  param(zeros(out)), param(zeros(out)))
-  cell.b.data[gate(out, 2)] .= 1
+  cell = LSTMCell(init(out * 4, in), init(out * 4, out), init(out * 4),
+                  zeros(out), zeros(out))
+  cell.b[gate(out, 2)] .= 1
  return cell
 end

@ -142,7 +128,7 @@ end

 hidden(m::LSTMCell) = (m.h, m.c)

-@treelike LSTMCell
+@functor LSTMCell

 Base.show(io::IO, l::LSTMCell) =
  print(io, "LSTMCell(", size(l.Wi, 2), ", ", size(l.Wi, 1)÷4, ")")
@ -168,8 +154,8 @@ mutable struct GRUCell{A,V}
 end

 GRUCell(in, out; init = glorot_uniform) =
-  GRUCell(param(init(out*3, in)), param(init(out*3, out)),
-          param(init(out*3)), param(zeros(out)))
+  GRUCell(init(out * 3, in), init(out * 3, out),
+          init(out * 3), zeros(out))

 function (m::GRUCell)(h, x)
  b, o = m.b, size(h, 1)
@ -183,7 +169,7 @@ end

 hidden(m::GRUCell) = m.h

-@treelike GRUCell
+@functor GRUCell

 Base.show(io::IO, l::GRUCell) =
  print(io, "GRUCell(", size(l.Wi, 2), ", ", size(l.Wi, 1)÷3, ")")
--- a/src/layers/stateless.jl
+++ b/src/layers/stateless.jl
@ -75,4 +75,3 @@ https://isaacchanghau.github.io/post/loss_functions/
 poisson(ŷ, y) = sum(ŷ .- y .* log.(ŷ)) *1 // size(y,2)

 hinge(ŷ, y) = sum(max.(0, 1 .-  ŷ .* y)) *1 // size(y,2)
-
--- a/src/onehot.jl
+++ b/src/onehot.jl
@ -37,7 +37,7 @@ import Adapt: adapt, adapt_structure

 adapt_structure(T, xs::OneHotMatrix) = OneHotMatrix(xs.height, adapt(T, xs.data))

-@init @require CuArrays="3a865a2d-5b23-5a0f-bc46-62713ec82fae" begin
+if has_cuarrays()
  import .CuArrays: CuArray, cudaconvert
  import Base.Broadcast: BroadcastStyle, ArrayStyle
  BroadcastStyle(::Type{<:OneHotMatrix{<:CuArray}}) = ArrayStyle{CuArray}()
@ -54,17 +54,19 @@ it will error.
 ## Examples

 ```jldoctest
+julia> using Flux: onehot
+
 julia> onehot(:b, [:a, :b, :c])
 3-element Flux.OneHotVector:
- false
-  true
- false
+ 0
+ 1
+ 0

 julia> onehot(:c, [:a, :b, :c])
 3-element Flux.OneHotVector:
- false
- false
-  true
+ 0
+ 0
+ 1
 ```
 """
 function onehot(l, labels)
@ -88,12 +90,13 @@ Create an [`OneHotMatrix`](@ref) with a batch of labels based on possible `label
 ## Examples

 ```jldoctest
-julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
-3×3 Flux.OneHotMatrix:
- false   true  false
-  true  false   true
- false  false  false
+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
+ 0  0  0
 ```
 """
 onehotbatch(ls, labels, unk...) =
@ -106,9 +109,9 @@ Base.argmax(xs::OneHotVector) = xs.ix

 Inverse operations of [`onehot`](@ref).

-## Examples
-
 ```jldoctest
+julia> using Flux: onecold
+
 julia> onecold([true, false, false], [:a, :b, :c])
 :a

@ -124,15 +127,6 @@ onecold(y::AbstractMatrix, labels...) =
 onecold(y::OneHotMatrix, labels...) =
  mapreduce(x -> Flux.onecold(x, labels...), |, y.data, dims = 2, init = 0)

-function argmax(xs...)
-  Base.depwarn("`argmax(...)` is deprecated, use `onecold(...)` instead.", :argmax)
-  return onecold(xs...)
-end
-
-# Ambiguity hack
-
-a::TrackedMatrix * b::OneHotVector = invoke(*, Tuple{AbstractMatrix,OneHotVector}, a, b)
-a::TrackedMatrix * b::OneHotMatrix = invoke(*, Tuple{AbstractMatrix,OneHotMatrix}, a, b)
-
-onecold(x::TrackedVector, l...) = onecold(data(x), l...)
-onecold(x::TrackedMatrix, l...) = onecold(data(x), l...)
+# 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...)
--- a/src/optimise/Optimise.jl
+++ b/src/optimise/Optimise.jl
@ -2,11 +2,10 @@ module Optimise

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

 include("optimisers.jl")
 include("train.jl")
-include("deprecations.jl")

 end
--- a/src/optimise/deprecations.jl
+++ b/src/optimise/deprecations.jl
@ -1,126 +0,0 @@
-using Base: depwarn
-using Flux: Params
-
-check_decay(opt, decay) = decay == 0 ? opt : Optimiser(opt, InvDecay(decay))
-
-# legacy update rule
-updaterule(opt, ps) = () -> _update_params!(opt, ps)
-
-function SGD(params::Union{AbstractArray, Params}, η = 0.1; decay = 0.)
-  depwarn("SGD(params) is deprecated; use Descent(η::Float64) instead", :SGD)
-
-  ps = params
-  opt = Descent(η)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function Momentum(params::Union{AbstractArray, Params}, η = 0.01; ρ = 0.9, decay = 0.)
-  depwarn("Momentum(params) is deprecated; use Momentum(η::Float64) instead", :Momentum)
-
-  ps = params
-  opt = Momentum(η, ρ)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function Nesterov(params::Union{AbstractArray, Params}, η = 0.001; ρ = 0.9, decay = 0.)
-  depwarn("Nesterov(params) is deprecated; use Nesterov(η::Float64) instead", :Nesterov)
-
-  ps = params
-  opt = Nesterov(η, ρ)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function RMSProp(params::Union{AbstractArray, Params}, η = 0.001; ρ = 0.9, decay = 0.)
-  depwarn("RMSProp(params) is deprecated; use RMSProp(η::Float64) instead", :RMSProp)
-
-  ps = params
-  opt = RMSProp(η, ρ)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function ADAM(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.)
-  depwarn("ADAM(params) is deprecated; use ADAM(η::Float64) instead", :ADAM)
-
-  ps = params
-  β = (β1, β2)
-  opt = ADAM(η, β)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function ADAGrad(params::Union{AbstractArray, Params}, η::Float64 = 0.1; decay = 0.)
-  depwarn("ADAGrad(params) is deprecated; use ADAGrad(η::Float64) instead", :ADAGrad)
-
-  ps = params
-  opt = ADAGrad(η)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function ADADelta(params::Union{AbstractArray, Params}, ρ::Float64 = 0.9; decay = 0.)
-  depwarn("ADADelta(params) is deprecated; use ADADelta(η::Float64) instead", :ADADelta)
-
-  ps = params
-  opt = ADADelta(ρ)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function AdaMax(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.)
-  depwarn("AdaMax(params) is deprecated; use AdaMax(η::Float64) instead", :AdaMax)
-
-  ps = params
-  β = (β1, β2)
-  opt = AdaMax(η, β)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function AMSGrad(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.)
-  depwarn("AMSGrad(params) is deprecated; use AMSGrad(η::Float64) instead", :AMSGrad)
-
-  ps = params
-  β = (β1, β2)
-  opt = AMSGrad(η, β)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function NADAM(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.)
-  depwarn("NADAM(params) is deprecated; use NADAM(η::Float64) instead", :NADAM)
-
-  ps = params
-  β = (β1, β2)
-  opt = NADAM(η, β)
-  opt = check_decay(opt, decay)
-  updaterule(opt, ps)
-end
-
-function ADAMW(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.)
-  depwarn("ADAMW(params) is deprecated; use ADAMW(η::Float64) instead", :ADAMW)
-
-  ps = params
-  β = (β1, β2)
-  opt = ADAMW(η, β)
-  opt = check_decay(opt, decay)
-  decay != 0 && (opt = Optimiser(opt, WeightDecay(decay)))
-  updaterule(opt, ps)
-end
-
-# Old training loop
-
-struct OldOptimiser
-  func
-end
-
-_update_params!(opt::OldOptimiser, ps) = opt.func()
-
-# Train function
-function train!(loss, data, opt; cb = () -> ())
-  depwarn("train!(loss, data, opt) is deprecated; use train!(loss, params, data, opt) instead", :train!)
-  train!(loss, (), data, OldOptimiser(opt); cb = cb)
-end
--- a/src/optimise/optimisers.jl
+++ b/src/optimise/optimisers.jl
@ -23,7 +23,7 @@ function apply!(o::Descent, x, Δ)
 end

 """
-    Momentum(params, η = 0.01; ρ = 0.9)
+    Momentum(η = 0.01; ρ = 0.9)

 Gradient descent with learning rate `η` and momentum `ρ`.
 """
@ -37,7 +37,7 @@ Momentum(η = 0.01, ρ = 0.9) = Momentum(η, ρ, IdDict())

 function apply!(o::Momentum, x, Δ)
  η, ρ = o.eta, o.rho
-  v = get!(o.velocity, x, zero(x))::typeof(data(x))
+  v = get!(o.velocity, x, zero(x))::typeof(x)
  @. v = ρ * v - η * Δ
  @. Δ = -v
 end
@ -57,7 +57,7 @@ Nesterov(η = 0.001, ρ = 0.9) = Nesterov(η, ρ, IdDict())

 function apply!(o::Nesterov, x, Δ)
  η, ρ = o.eta, o.rho
-  v = get!(o.velocity, x, zero(x))::typeof(data(x))
+  v = get!(o.velocity, x, zero(x))::typeof(x)
  d = @. ρ^2 * v - (1+ρ) * η * Δ
  @. v = ρ*v - η*Δ
  @. Δ = -d
@ -80,7 +80,7 @@ RMSProp(η = 0.001, ρ = 0.9) = RMSProp(η, ρ, IdDict())

 function apply!(o::RMSProp, x, Δ)
  η, ρ = o.eta, o.rho
-  acc = get!(o.acc, x, zero(x))::typeof(data(x))
+  acc = get!(o.acc, x, zero(x))::typeof(x)
  @. acc = ρ * acc + (1 - ρ) * Δ^2
  @. Δ *= η / (√acc + ϵ)
 end
@ -108,6 +108,36 @@ function apply!(o::ADAM, x, Δ)
  return Δ
 end

+"""
+    RADAM(η = 0.001, β = (0.9, 0.999))
+
+[RADAM](https://arxiv.org/pdf/1908.03265v1.pdf) optimiser (Rectified ADAM).
+"""
+mutable struct RADAM
+  eta::Float64
+  beta::Tuple{Float64,Float64}
+  state::IdDict
+end
+
+RADAM(η = 0.001, β = (0.9, 0.999)) = RADAM(η, β, IdDict())
+
+function apply!(o::RADAM, x, Δ)
+  η, β = o.eta, o.beta
+  ρ∞ = 2/(1-β[2])-1
+  mt, vt, βp, t = get!(o.state, x, (zero(x), zero(x), β, 1))
+  @. mt = β[1] * mt + (1 - β[1]) * Δ
+  @. vt = β[2] * vt + (1 - β[2]) * Δ^2
+  ρ = ρ∞ - 2t*βp[2]/(1-βp[2])
+  if ρ > 4
+    r = sqrt((ρ-4)*(ρ-2)*ρ∞/((ρ∞-4)*(ρ∞-2)*ρ))
+    @. Δ =  mt / (1 - βp[1]) / (√(vt / (1 - βp[2])) + ϵ) * η * r
+  else
+    @. Δ =  mt / (1 - βp[1]) * η
+  end
+  o.state[x] = (mt, vt, βp .* β, t+1)
+  return Δ
+end
+
 """
    AdaMax(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08)

@ -147,7 +177,7 @@ ADAGrad(η = 0.1) = ADAGrad(η, IdDict())

 function apply!(o::ADAGrad, x, Δ)
  η = o.eta
-  acc = get!(o.acc, x, fill(ϵ, size(x)))::typeof(data(x))
+  acc = get!(o.acc, x, fill(ϵ, size(x)))::typeof(x)
  @. acc += Δ^2
  @. Δ *= η / (√acc + ϵ)
 end
@ -322,5 +352,5 @@ WeightDecay() = WeightDecay(0)

 function apply!(o::WeightDecay, x, Δ)
  wd = o.wd
-  @. Δ += wd * data(x)
+  @. Δ += wd * x
 end
--- a/src/optimise/train.jl
+++ b/src/optimise/train.jl
@ -1,32 +1,29 @@
 using Juno
-import Flux.Tracker: Params, gradient, data, update!
-import Base.depwarn
+import Zygote: Params, gradient
+
+function update!(x::AbstractArray, x̄)
+  x .+= x̄
+  return x
+end

 function update!(opt, x, x̄)
-  update!(x, -apply!(opt, x, data(x̄)))
+  x .-= apply!(opt, x, x̄)
 end

 function update!(opt, xs::Params, gs)
  for x in xs
+    gs[x] == nothing && continue
    update!(opt, x, gs[x])
  end
 end

-# Added as an internal API but everyone started using it.
-function _update_params!(opt, xs)
-  depwarn("`_update_params!` is deprecated, use `update!` instead.", :stop)
-  for x in xs
-    update!(opt, x, Tracker.grad(x))
-    x.tracker.grad = Tracker.zero_grad!(x.tracker.grad)
-  end
-end
-
 # Callback niceties
 call(f, xs...) = f(xs...)
 runall(f) = f
 runall(fs::AbstractVector) = () -> foreach(call, fs)

 struct StopException <: Exception end
+
 """
    stop()

@ -72,10 +69,7 @@ function train!(loss, ps, data, opt; cb = () -> ())
        loss(d...)
      end
      update!(opt, ps, gs)
-      if cb() == :stop
-        depwarn("Use of `:stop` is deprecated; use `Flux.stop()` instead", :stop)
-        break
-      end
+      cb()
    catch ex
      if ex isa StopException
        break
--- a/src/treelike.jl
+++ b/src/treelike.jl
@ -1,87 +0,0 @@
-import Adapt: adapt, adapt_storage
-import .Tracker: IdSet
-
-children(x) = ()
-mapchildren(f, x) = x
-
-children(x::Tuple) = x
-children(x::NamedTuple) = x
-mapchildren(f, x::Tuple) = map(f, x)
-mapchildren(f, x::NamedTuple) = map(f, x)
-
-function treelike(m::Module, T, fs = fieldnames(T))
-  @eval m begin
-    Flux.children(x::$T) = ($([:(x.$f) for f in fs]...),)
-    Flux.mapchildren(f, x::$T) = $T(f.($children(x))...)
-  end
-end
-
-macro treelike(T, fs = nothing)
-  fs == nothing || isexpr(fs, :tuple) || error("@treelike T (a, b)")
-  fs = fs == nothing ? [] : [:($(map(QuoteNode, fs.args)...),)]
-  :(treelike(@__MODULE__, $(esc(T)), $(fs...)))
-end
-
-isleaf(x) = isempty(children(x))
-
-function mapleaves(f, x; cache = IdDict())
-  haskey(cache, x) && return cache[x]
-  cache[x] = isleaf(x) ? f(x) : mapchildren(x -> mapleaves(f, x, cache = cache), x)
-end
-
-function prefor(f, x; seen = IdSet())
-  x ∈ seen && return
-  f(x)
-  foreach(x -> prefor(f, x, seen = seen), children(x))
-  return
-end
-
-function params(m)
-  ps = Params()
-  prefor(p ->
-    Tracker.istracked(p) && Tracker.isleaf(p) &&
-      !any(p′ -> p′ === p, ps) && push!(ps, p),
-    m)
-  return ps
-end
-
-params(m...) = params(m)
-
-function loadparams!(m, xs)
-  for (p, x) in zip(params(m), xs)
-    size(p) == size(x) ||
-      error("Expected param size $(size(p)), got $(size(x))")
-    copyto!(data(p), data(x))
-  end
-end
-
-# CPU/GPU movement conveniences
-
-cpu(m) = mapleaves(x -> adapt(Array, x), m)
-
-gpu_adaptor = identity
-
-@init @require CuArrays="3a865a2d-5b23-5a0f-bc46-62713ec82fae" begin
-  global gpu_adaptor = CuArrays.cu
-end
-
-gpu(x) = mapleaves(gpu_adaptor, x)
-
-# Precision
-
-adapt_storage(T::Type{<:Real}, xs::AbstractArray{<:Real}) = convert.(T, xs)
-
-paramtype(T::Type{<:Real}, m) = mapleaves(x -> adapt(T, x), m)
-
-f32(m) = paramtype(Float32, m)
-f64(m) = paramtype(Float64, m)
-
-# General parameter map
-
-function mapparams(f, m)
-  mapleaves(m) do x
-    Tracker.istracked(x) ? param(f(Tracker.data(x))) :
-    x isa Union{AbstractArray,Number} ? f(x) :
-    x
-  end
-end
--- a/test/cuda/cuda.jl
+++ b/test/cuda/cuda.jl
@ -1,4 +1,5 @@
-using Flux, Flux.Tracker, CuArrays, Test
+using Flux, Test
+using Flux.CuArrays
 using Flux: gpu

@info "Testing GPU Support"
@ -7,11 +8,11 @@ using Flux: gpu

 CuArrays.allowscalar(false)

-x = param(randn(5, 5))
+x = randn(5, 5)
 cx = gpu(x)
-@test cx isa TrackedArray && cx.data isa CuArray
+@test cx isa CuArray

-@test Flux.onecold(param(gpu([1.,2.,3.]))) == 3
+@test Flux.onecold(gpu([1.0, 2.0, 3.0])) == 3

 x = Flux.onehotbatch([1, 2, 3], 1:3)
 cx = gpu(x)
@ -21,24 +22,26 @@ cx = gpu(x)
 m = Chain(Dense(10, 5, tanh), Dense(5, 2), softmax)
 cm = gpu(m)

-@test all(p isa TrackedArray && p.data isa CuArray for p in params(cm))
-@test cm(gpu(rand(10, 10))) isa TrackedArray{Float32,2,CuArray{Float32,2}}
+@test all(p isa CuArray for p in params(cm))
+@test cm(gpu(rand(10, 10))) isa CuArray{Float32,2}

 x = [1,2,3]
 cx = gpu(x)
@test Flux.crossentropy(x,x) ≈ Flux.crossentropy(cx,cx)

-xs = param(rand(5,5))
+xs = rand(5, 5)
 ys = Flux.onehotbatch(1:5,1:5)
@test collect(cu(xs) .+ cu(ys)) ≈ collect(xs .+ ys)

 c = gpu(Conv((2,2),3=>4))
+x = gpu(rand(10, 10, 3, 2))
 l = c(gpu(rand(10,10,3,2)))
-Flux.back!(sum(l))
+@test gradient(x -> sum(c(x)), x)[1] isa CuArray

 c = gpu(CrossCor((2,2),3=>4))
+x = gpu(rand(10, 10, 3, 2))
 l = c(gpu(rand(10,10,3,2)))
-Flux.back!(sum(l))
+@test gradient(x -> sum(c(x)), x)[1] isa CuArray

 end

@ -49,9 +52,7 @@ end
 end

 if CuArrays.libcudnn != nothing
-    @info "Testing Flux/CUDNN"
-    include("cudnn.jl")
-    if !haskey(ENV, "CI_DISABLE_CURNN_TEST")
-      include("curnn.jl")
-    end
+  @info "Testing Flux/CUDNN"
+  include("cudnn.jl")
+  include("curnn.jl")
 end
--- a/test/cuda/cudnn.jl
+++ b/test/cuda/cudnn.jl
@ -1,48 +1,44 @@
-using Flux, Flux.Tracker, CuArrays, Test
-using Flux.Tracker: TrackedArray, data
+using Flux, CuArrays, Test
+using Flux: pullback

@testset "CUDNN BatchNorm" begin
    @testset "4D Input" begin
-        x = TrackedArray(Float64.(collect(reshape(1:12, 2, 2, 3, 1))))
+        x = Float64.(collect(reshape(1:12, 2, 2, 3, 1)))
        m = BatchNorm(3)
        cx = gpu(x)
        cm = gpu(m)

-        y = m(x)
-        cy = cm(cx)
+        y, back = pullback((m, x) -> m(x), m, x)
+        cy, cback = pullback((m, x) -> m(x), cm, cx)

-        @test cy isa TrackedArray{Float32,4,CuArray{Float32,4}}
+        @test cpu(cy) ≈ y

-        @test cpu(data(cy)) ≈ data(y)
+        Δ = randn(size(y))
+        dm, dx = back(Δ)
+        cdm, cdx = cback(gpu(Δ))

-        g = rand(size(y)...)
-        Flux.back!(y, g)
-        Flux.back!(cy, gpu(g))
-
-        @test m.γ.grad ≈ cpu(cm.γ.grad)
-        @test m.β.grad ≈ cpu(cm.β.grad)
-        @test x.grad ≈ cpu(x.grad)
+        @test dm[].γ ≈ cpu(cdm[].γ)
+        @test dm[].β ≈ cpu(cdm[].β)
+        @test dx ≈ cpu(cdx)
    end

    @testset "2D Input" begin
-        x = TrackedArray(Float64.(collect(reshape(1:12, 3, 4))))
+        x = Float64.(collect(reshape(1:12, 3, 4)))
        m = BatchNorm(3)
        cx = gpu(x)
        cm = gpu(m)

-        y = m(x)
-        cy = cm(cx)
+        y, back = pullback((m, x) -> m(x), m, x)
+        cy, cback = pullback((m, x) -> m(x), cm, cx)

-        @test cy isa TrackedArray{Float32,2,CuArray{Float32,2}}
+        @test cpu(cy) ≈ y

-        @test cpu(data(cy)) ≈ data(y)
+        Δ = randn(size(y))
+        dm, dx = back(Δ)
+        cdm, cdx = cback(gpu(Δ))

-        g = rand(size(y)...)
-        Flux.back!(y, g)
-        Flux.back!(cy, gpu(g))
-
-        @test m.γ.grad ≈ cpu(cm.γ.grad)
-        @test m.β.grad ≈ cpu(cm.β.grad)
-        @test x.grad ≈ cpu(x.grad)
+        @test dm[].γ ≈ cpu(cdm[].γ)
+        @test dm[].β ≈ cpu(cdm[].β)
+        @test dx ≈ cpu(cdx)
    end
 end
--- a/test/cuda/curnn.jl
+++ b/test/cuda/curnn.jl
@ -1,46 +1,63 @@
 using Flux, CuArrays, Test
+using Flux: pullback
+
+@testset for R in [RNN, GRU, LSTM]
+  m = R(10, 5) |> gpu
+  x = gpu(rand(10))
+  (m̄,) = gradient(m -> sum(m(x)), m)
+  Flux.reset!(m)
+  θ = gradient(() -> sum(m(x)), params(m))
+  @test collect(m̄[].cell[].Wi) == collect(θ[m.cell.Wi])
+end

@testset "RNN" begin
-  @testset for R in [RNN, GRU, LSTM]
+  @testset for R in [RNN, GRU, LSTM], batch_size in (1, 5)
    rnn = R(10, 5)
-    curnn = mapleaves(gpu, rnn)
-    @testset for batch_size in (1, 5)
-      Flux.reset!(rnn)
-      Flux.reset!(curnn)
-      x = batch_size == 1 ?
-        param(rand(10)) :
-        param(rand(10,batch_size))
-      cux = gpu(x)
-      y = (rnn(x); rnn(x))
-      cuy = (curnn(cux); curnn(cux))
+    curnn = fmap(gpu, rnn)

-      @test y.data ≈ collect(cuy.data)
-      @test haskey(Flux.CUDA.descs, curnn.cell)
+    Flux.reset!(rnn)
+    Flux.reset!(curnn)
+    x = batch_size == 1 ?
+      rand(10) :
+      rand(10, batch_size)
+    cux = gpu(x)

-      Δ = randn(size(y))
+    y, back = pullback((r, x) -> r(x), rnn, x)
+    cuy, cuback = pullback((r, x) -> r(x), curnn, cux)

-      Flux.back!(y, Δ)
-      Flux.back!(cuy, gpu(Δ))
+    @test y ≈ collect(cuy)
+    @test haskey(Flux.CUDA.descs, curnn.cell)

-      @test x.grad ≈ collect(cux.grad)
-      @test rnn.cell.Wi.grad ≈ collect(curnn.cell.Wi.grad)
-      @test rnn.cell.Wh.grad ≈ collect(curnn.cell.Wh.grad)
-      @test rnn.cell.b.grad ≈ collect(curnn.cell.b.grad)
-      @test rnn.cell.h.grad ≈ collect(curnn.cell.h.grad)
-      if isdefined(rnn.cell, :c)
-        @test rnn.cell.c.grad ≈ collect(curnn.cell.c.grad)
+    ȳ = randn(size(y))
+    m̄, x̄ = back(ȳ)
+    cum̄, cux̄ = cuback(gpu(ȳ))
+
+    m̄[].cell[].Wi
+
+    m̄[].state
+    cum̄[].state
+
+    @test x̄ ≈ collect(cux̄)
+    @test m̄[].cell[].Wi ≈ collect(cum̄[].cell[].Wi)
+    @test m̄[].cell[].Wh ≈ collect(cum̄[].cell[].Wh)
+    @test m̄[].cell[].b ≈ collect(cum̄[].cell[].b)
+    if m̄[].state isa Tuple
+      for (x, cx) in zip(m̄[].state, cum̄[].state)
+        @test x ≈ collect(cx)
      end
-
-      Flux.reset!(rnn)
-      Flux.reset!(curnn)
-      ohx = batch_size == 1 ?
-        Flux.onehot(rand(1:10), 1:10) :
-        Flux.onehotbatch(rand(1:10, batch_size), 1:10)
-      cuohx = gpu(ohx)
-      y = (rnn(ohx); rnn(ohx))
-      cuy = (curnn(cuohx); curnn(cuohx))
-
-      @test y.data ≈ collect(cuy.data)
+    else
+      @test m̄[].state ≈ collect(cum̄[].state)
    end
+
+    Flux.reset!(rnn)
+    Flux.reset!(curnn)
+    ohx = batch_size == 1 ?
+      Flux.onehot(rand(1:10), 1:10) :
+      Flux.onehotbatch(rand(1:10, batch_size), 1:10)
+    cuohx = gpu(ohx)
+    y = (rnn(ohx); rnn(ohx))
+    cuy = (curnn(cuohx); curnn(cuohx))
+
+    @test y ≈ collect(cuy)
  end
 end
--- a/test/layers/conv.jl
+++ b/test/layers/conv.jl
@ -25,9 +25,9 @@ end
@testset "asymmetric padding" begin
  r = ones(Float32, 28, 28, 1, 1)
  m = Conv((3, 3), 1=>1, relu; pad=(0,1,1,2))
-  m.weight.data[:] .= 1.0
-  m.bias.data[:] .= 0.0
-  y_hat = Flux.data(m(r))[:,:,1,1]
+  m.weight[:] .= 1.0
+  m.bias[:] .= 0.0
+  y_hat = m(r)[:,:,1,1]
  @test size(y_hat) == (27, 29)
  @test y_hat[1, 1] ≈ 6.0
  @test y_hat[2, 2] ≈ 9.0
@ -41,7 +41,7 @@ end
  r = zeros(Float32, 28, 28, 3, 5)
  m1 = DepthwiseConv((2, 2), 3=>15)
  @test size(m1(r), 3) == 15
-  
+
  m3 = DepthwiseConv((2, 3), 3=>9)
  @test size(m3(r), 3) == 9

@ -62,7 +62,7 @@ end
  y = CrossCor(w, [0.0])

  @test sum(w .* x[1:2, 1:2, :, :]) == y(x)[1, 1, 1, 1]
-  
+
  r = zeros(Float32, 28, 28, 1, 5)
  m = Chain(
    CrossCor((2, 2), 1=>16, relu),
@ -102,4 +102,3 @@ end
    true
  end
 end
-
--- a/test/layers/normalisation.jl
+++ b/test/layers/normalisation.jl
@ -1,29 +1,29 @@
-using Flux: testmode!
-using Flux.Tracker: data
+using Flux, Test, Statistics
+using Zygote: pullback
+
+trainmode(f, x...) = pullback(f, x...)[1]
+trainmode(f) = (x...) -> trainmode(f, x...)

@testset "Dropout" begin
  x = [1.,2.,3.]
-  @test x == testmode!(Dropout(0.1))(x)
-  @test x == Dropout(0)(x)
-  @test zero(x) == Dropout(1)(x)
+  @test x == Dropout(0.1)(x)
+  @test x == trainmode(Dropout(0), x)
+  @test zero(x) == trainmode(Dropout(1), x)

  x = rand(100)
  m = Dropout(0.9)
-  y = m(x)
+  y = trainmode(m, x)
  @test count(a->a==0, y) > 50
-  testmode!(m)
  y = m(x)
  @test count(a->a==0, y) == 0
-  testmode!(m, false)
-  y = m(x)
+  y = trainmode(m, x)
  @test count(a->a==0, y) > 50

-  x = rand(100)
+  x = rand(Float32, 100)
  m = Chain(Dense(100,100),
            Dropout(0.9))
-  y = m(x)
+  y = trainmode(m, x)
  @test count(a->a == 0, y) > 50
-  testmode!(m)
  y = m(x)
  @test count(a->a == 0, y) == 0

@ -39,18 +39,18 @@ using Flux.Tracker: data
 end

@testset "BatchNorm" begin
-  let m = BatchNorm(2), x = param([1 3 5;
-                                   2 4 6])
+  let m = BatchNorm(2), x = [1.0 3.0 5.0;
+                             2.0 4.0 6.0]

-    @test m.β.data == [0, 0]  # initβ(2)
-    @test m.γ.data == [1, 1]  # initγ(2)
+    @test length(params(m)) == 2
+
+    @test m.β == [0, 0]  # initβ(2)
+    @test m.γ == [1, 1]  # initγ(2)
    # initial m.σ is 1
    # initial m.μ is 0
-    @test m.active
-
-    # @test m(x).data ≈ [-1 -1; 0 0; 1 1]'
-    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}:
    #  1.0  3.0  5.0
@ -69,41 +69,32 @@ end
    # 2×1 Array{Float64,2}:
    #  1.3
    #  1.3
-    @test m.σ² ≈ .1 .* var(x.data, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]
+    @test m.σ² ≈ .1 .* var(x, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]

-    testmode!(m)
-    @test !m.active
-
-    x′ = m(x).data
+    x′ = m(x)
    @test isapprox(x′[1], (1 .- 0.3) / sqrt(1.3), atol = 1.0e-5)
  end

  # with activation function
-  let m = BatchNorm(2, sigmoid), x = param([1 3 5;
-                                            2 4 6])
-    @test m.active
-    m(x)
-
-    testmode!(m)
-    @test !m.active
-
-    y = m(x).data
-    @test isapprox(y, data(sigmoid.((x .- m.μ) ./ sqrt.(m.σ² .+ m.ϵ))), atol = 1.0e-7)
+  let m = BatchNorm(2, sigmoid), x = [1.0 3.0 5.0;
+                                      2.0 4.0 6.0]
+    y = m(x)
+    @test isapprox(y, sigmoid.((x .- m.μ) ./ sqrt.(m.σ² .+ m.ϵ)), atol = 1.0e-7)
  end

-  let m = BatchNorm(2), x = param(reshape(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 = BatchNorm(2), x = param(reshape(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 = BatchNorm(2), x = param(reshape(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
@ -115,20 +106,18 @@ end
  end
 end

-
@testset "InstanceNorm" begin
  # helper functions
  expand_inst = (x, as) -> reshape(repeat(x, outer=[1, as[length(as)]]), as...)
  # begin tests
  let m = InstanceNorm(2), sizes = (3, 2, 2),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
+        x = reshape(collect(1:prod(sizes)), sizes)

-      @test m.β.data == [0, 0]  # initβ(2)
-      @test m.γ.data == [1, 1]  # initγ(2)
-
-      @test m.active
-
-      m(x)
+      @test length(params(m)) == 2
+      x = Float64.(x)
+      @test m.β == [0, 0]  # initβ(2)
+      @test m.γ == [1, 1]  # initγ(2)
+      y = trainmode(m, x)

      #julia> x
      #[:, :, 1] =
@ -153,37 +142,28 @@ end
      # (1. - .1) * 0 + .1 * (5. + 11.) / 2 = .8
      @test m.μ ≈ [0.5, 0.8]
      # momentum * var * num_items / (num_items - 1) + (1 - momentum) * sigma_sq
-      # julia> reshape(mean(.1 .* var(x.data, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1.
+      # julia> reshape(mean(.1 .* var(x, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1.
      # 2-element Array{Float64,1}:
      #  1.
      #  1.
-      @test m.σ² ≈ reshape(mean(.1 .* var(x.data, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1.
+      @test m.σ² ≈ reshape(mean(.1 .* var(x, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1.

-      testmode!(m)
-      @test !m.active
-
-      x′ = m(x).data
+      x′ = m(x)
      @test isapprox(x′[1], (1 - 0.5) / sqrt(1. + 1f-5), atol = 1.0e-5)
  end
  # with activation function
  let m = InstanceNorm(2, sigmoid), sizes = (3, 2, 2),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
-
+      x = reshape(collect(1:prod(sizes)), sizes)
+    x = Float64.(x)
    affine_shape = collect(sizes)
    affine_shape[1] = 1

-    @test m.active
-    m(x)
-
-    testmode!(m)
-    @test !m.active
-
-    y = m(x).data
-    @test isapprox(y, data(sigmoid.((x .- expand_inst(m.μ, affine_shape)) ./ sqrt.(expand_inst(m.σ², affine_shape) .+ m.ϵ))), atol = 1.0e-7)
+    y = m(x)
+    @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 = InstanceNorm(2), sizes = (2, 4, 1, 2, 3),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
+  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...)
    @test m(x) == y
@ -191,16 +171,16 @@ 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 = param(reshape(collect(1:prod(sizes)), sizes))
-    y = m(x)
+      x = reshape(Float32.(collect(1:prod(sizes))), sizes)
+    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 = InstanceNorm(2), m_bnorm = BatchNorm(12), sizes = (5, 5, 3, 4, 2, 6),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
+  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

@ -216,14 +196,14 @@ end
  squeeze(x) = dropdims(x, dims = tuple(findall(size(x) .== 1)...)) # To remove all singular dimensions

  let m = GroupNorm(4,2), sizes = (3,4,2),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
+        x = reshape(collect(1:prod(sizes)), sizes)

-      @test m.β.data == [0, 0, 0, 0]  # initβ(32)
-      @test m.γ.data == [1, 1, 1, 1]  # initγ(32)
+      @test length(params(m)) == 2
+      x = Float64.(x)
+      @test m.β == [0, 0, 0, 0]  # initβ(32)
+      @test m.γ == [1, 1, 1, 1]  # initγ(32)

-      @test m.active
-
-      m(x)
+      y = trainmode(m, x)

      #julia> x
      #[:, :, 1]  =
@ -243,7 +223,7 @@ end
      # (13. + 14. + 15. + 16. + 17. + 18.) / 6 = 15.5
      # (19. + 20. + 21. + 22. + 23. + 24.) / 6 = 21.5
      #
-      # μ = 
+      # μ =
      # 3.5   15.5
      # 9.5   21.5
      #
@ -253,46 +233,37 @@ end
      @test m.μ ≈ [0.95, 1.55]

      # julia> mean(var(reshape(x,3,2,2,2),dims=(1,2)).* .1,dims=2) .+ .9*1.
-      # 2-element Array{Tracker.TrackedReal{Float64},1}:
+      # 2-element Array{Float64,1}:
      #  1.25
      #  1.25
      @test m.σ² ≈ mean(squeeze(var(reshape(x,3,2,2,2),dims=(1,2))).*.1,dims=2) .+ .9*1.

-      testmode!(m)
-      @test !m.active
-
-      x′ = m(x).data
+      x′ = m(x)
      @test isapprox(x′[1], (1 - 0.95) / sqrt(1.25 + 1f-5), atol = 1.0e-5)
  end
  # with activation function
  let m = GroupNorm(4,2, sigmoid), sizes = (3, 4, 2),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
-
+      x = reshape(collect(1:prod(sizes)), sizes)
+    x = Float64.(x)
    μ_affine_shape = ones(Int,length(sizes) + 1)
    μ_affine_shape[end-1] = 2 # Number of groups

    affine_shape = ones(Int,length(sizes) + 1)
-    affine_shape[end-2] = 2 # Channels per group 
+    affine_shape[end-2] = 2 # Channels per group
    affine_shape[end-1] = 2 # Number of groups
    affine_shape[1] = sizes[1]
    affine_shape[end] = sizes[end]

    og_shape = size(x)

-    @test m.active
-    m(x)
-
-    testmode!(m)
-    @test !m.active
-
    y = m(x)
    x_ = reshape(x,affine_shape...)
-    out = reshape(data(sigmoid.((x_ .- reshape(m.μ,μ_affine_shape...)) ./ sqrt.(reshape(m.σ²,μ_affine_shape...) .+ m.ϵ))),og_shape)
+    out = reshape(sigmoid.((x_ .- reshape(m.μ,μ_affine_shape...)) ./ sqrt.(reshape(m.σ²,μ_affine_shape...) .+ m.ϵ)),og_shape)
    @test isapprox(y, out, atol = 1.0e-7)
  end

-  let m = GroupNorm(2,2), sizes = (2, 4, 1, 2, 3),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
+  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...)
    @test m(x) == y
@ -300,22 +271,22 @@ end

  # 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 = param(reshape(collect(1:prod(sizes)), sizes))
-    y = m(x)
+      x = Float32.(reshape(collect(1:prod(sizes)), sizes))
+    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 = InstanceNorm(4), GN = GroupNorm(4,4), sizes = (2,2,3,4,5),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
+  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 = BatchNorm(4), GN = GroupNorm(4,4), sizes = (2,2,3,4,1),
-      x = param(reshape(collect(1:prod(sizes)), sizes))
+  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

--- a/test/layers/stateless.jl
+++ b/test/layers/stateless.jl
@ -72,13 +72,13 @@ const ϵ = 1e-7
  end
  
  @testset "no spurious promotions" begin
-    for T in (Float16, Float32, Float64)
+    for T in (Float32, Float64)
      y = rand(T, 2)
      ŷ = rand(T, 2)
      for f in (mse, crossentropy, logitcrossentropy)
-        fwd, back = Flux.Tracker.forward(mse, ŷ, y)
-        @test typeof(fwd) == Flux.Tracker.TrackedReal{T}
-        @test eltype(back(one(T))[1]) == Flux.Tracker.TrackedReal{T}
+        fwd, back = Flux.pullback(f, ŷ, y)
+        @test fwd isa T
+        @test eltype(back(one(T))[1]) == T
      end
    end
  end
--- a/test/optimise.jl
+++ b/test/optimise.jl
@ -1,42 +1,44 @@
 using Flux.Optimise
 using Flux.Optimise: runall
-using Flux.Tracker
+using Flux: Params, gradient
 using Test
+
@testset "Optimise" begin
  w = randn(10, 10)
  @testset for opt in [ADAMW(), ADAGrad(0.1), AdaMax(), ADADelta(0.9), AMSGrad(),
-                       NADAM(), Descent(0.1), ADAM(), Nesterov(), RMSProp(),
+                       NADAM(), RADAM(), Descent(0.1), ADAM(), Nesterov(), RMSProp(),
                       Momentum()]
-    w′ = param(randn(10, 10))
+    w′ = randn(10, 10)
    loss(x) = Flux.mse(w*x, w′*x)
    for t = 1: 10^5
      θ = Params([w′])
-      θ̄ = gradient(() -> loss(rand(10)), θ)
+      x = rand(10)
+      θ̄ = gradient(() -> loss(x), θ)
      Optimise.update!(opt, θ, θ̄)
    end
-    @test Flux.mse(w, w′) < 0.01
+    @test loss(rand(10, 10)) < 0.01
  end
 end

@testset "Optimiser" begin
  w = randn(10, 10)
  @testset for Opt in [InvDecay, WeightDecay, ExpDecay]
-    w′ = param(randn(10, 10))
+    w′ = randn(10, 10)
    loss(x) = Flux.mse(w*x, w′*x)
    opt = Optimiser(Opt(), ADAM(0.001))
    for t = 1:10^5
-      l = loss(rand(10))
-      back!(l)
-      delta = Optimise.apply!(opt, w′.data, w′.grad)
-      w′.data .-= delta
+      θ = Params([w′])
+      x = rand(10)
+      θ̄ = gradient(() -> loss(x), θ)
+      Optimise.update!(opt, θ, θ̄)
    end
-    @test Flux.mse(w, w′) < 0.01
+    @test loss(rand(10, 10)) < 0.01
  end
 end

@testset "Training Loop" begin
  i = 0
-  l = param(1)
+  l = 1

  Flux.train!(() -> (sleep(0.1); i += 1; l),
              (),
@ -57,17 +59,18 @@ end
@testset "ExpDecay" begin
    w = randn(10, 10)
    o = ExpDecay(0.1, 0.1, 1000, 1e-4)
-    w1 = param(randn(10,10))
+    w1 = randn(10,10)
    loss(x) = Flux.mse(w*x, w1*x)
    flag = 1
    decay_steps = []
    for t = 1:10^5
-      l = loss(rand(10))
-      back!(l)
      prev_eta = o.eta
-      prev_grad = collect(w1.grad)
-      delta = Optimise.apply!(o, w1.data, w1.grad)
-      w1.data .-= delta
+      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)
--- a/test/runtests.jl
+++ b/test/runtests.jl
@ -1,11 +1,8 @@
-using Flux, Test, Random, Statistics
+using Flux, Test, Random, Statistics, Documenter
 using Random

 Random.seed!(0)

-# So we can use the system CuArrays
-insert!(LOAD_PATH, 2, "@v#.#")
-
@testset "Flux" begin

@info "Testing Basics"
@ -22,12 +19,14 @@ include("layers/normalisation.jl")
 include("layers/stateless.jl")
 include("layers/conv.jl")

-@info "Running Gradient Checks"
-
-include("tracker.jl")
-
-if Base.find_package("CuArrays") != nothing
+if isdefined(Flux, :CUDA)
  include("cuda/cuda.jl")
+else
+  @warn "CUDA unavailable, not testing GPU support"
+end
+
+if VERSION >= v"1.2"
+  doctest(Flux)
 end

 end
--- a/test/tracker.jl
+++ b/test/tracker.jl
@ -1,15 +0,0 @@
-using Flux, Test
-using Tracker: gradcheck
-
-gradtest(f, xs::AbstractArray...) = gradcheck((xs...) -> sum(sin.(f(xs...))), xs...)
-gradtest(f, dims...) = gradtest(f, rand.(Float64, dims)...)
-
-@testset "Tracker" begin
-
-@test gradtest(Flux.mse, rand(5,5), rand(5, 5))
-@test gradtest(Flux.crossentropy, rand(5,5), rand(5, 5))
-
-@test gradtest(x -> Flux.normalise(x), rand(4,3))
-@test gradtest(x -> Flux.normalise(x, dims = 2), rand(3,4))
-
-end
--- a/test/utils.jl
+++ b/test/utils.jl
@ -1,5 +1,5 @@
 using Flux
-using Flux: throttle, jacobian, glorot_uniform, glorot_normal, stack, unstack
+using Flux: throttle, glorot_uniform, glorot_normal, stack, unstack
 using StatsBase: std
 using Random
 using Test
@ -52,15 +52,6 @@ using Test
  end
 end

-@testset "Jacobian" begin
-  A = param(randn(2,2))
-  x = randn(2)
-  m(x) = A*x
-  y = m(x)
-  J = jacobian(m,x)
-  @test J ≈ A.data
-end
-
@testset "Initialization" begin
  # Set random seed so that these tests don't fail randomly
  Random.seed!(0)
@ -85,6 +76,15 @@ end
  @test size.(params(m)) == [(5, 10), (5,)]
  m = RNN(10, 5)
  @test size.(params(m)) == [(5, 10), (5, 5), (5,), (5,)]
+
+  # Layer duplicated in same chain, params just once pls.
+  c = Chain(m, m)
+  @test size.(params(c)) == [(5, 10), (5, 5), (5,), (5,)]
+
+  # Self-referential array. Just want params, no stack overflow pls.
+  r = Any[nothing,m]
+  r[1] = r
+  @test size.(params(r)) == [(5, 10), (5, 5), (5,), (5,)]
 end

@testset "Basic Stacking" begin
@ -96,12 +96,11 @@ end
@testset "Precision" begin
  m = Chain(Dense(10, 5, relu), Dense(5, 2))
  x = rand(10)
-  @test eltype(m[1].W.data) == Float32
-  @test eltype(m(x).data) == Float32
-  @test eltype(f64(m)(x).data) == Float64
-  @test eltype(f64(m)[1].W.data) == Float64
-  @test eltype(f32(f64(m))[1].W.data) == Float32
-  @test Tracker.isleaf(f32(f64(m))[1].W)
+  @test eltype(m[1].W) == Float32
+  @test eltype(m(x)) == Float32
+  @test eltype(f64(m)(x)) == Float64
+  @test eltype(f64(m)[1].W) == Float64
+  @test eltype(f32(f64(m))[1].W) == Float32
 end

@testset "Stacking" begin