change train

.gitignore

@@ -4,3 +4,4 @@
 docs/build/
 docs/site/
 deps
+Manifest.toml

Manifest.toml

@@ -1,367 +0,0 @@
-# This file is machine-generated - editing it directly is not advised
-
-[[AbstractFFTs]]
-deps = ["LinearAlgebra"]
-git-tree-sha1 = "051c95d6836228d120f5f4b984dd5aba1624f716"
-uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
-version = "0.5.0"
-
-[[AbstractTrees]]
-deps = ["Markdown"]
-git-tree-sha1 = "86d092c2599f1f7bb01668bf8eb3412f98d61e47"
-uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
-version = "0.3.2"
-
-[[Adapt]]
-deps = ["LinearAlgebra"]
-git-tree-sha1 = "c88cfc7f9c1f9f8633cddf0b56e86302b70f64c5"
-uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "1.0.1"
-
-[[ArrayLayouts]]
-deps = ["FillArrays", "LinearAlgebra"]
-git-tree-sha1 = "41956a49a8a4fefa1bf6664bca4a3035aba4c3a0"
-uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
-version = "0.2.3"
-
-[[Base64]]
-uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
-
-[[BinaryProvider]]
-deps = ["Libdl", "SHA"]
-git-tree-sha1 = "5b08ed6036d9d3f0ee6369410b830f8873d4024c"
-uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
-version = "0.5.8"
-
-[[CEnum]]
-git-tree-sha1 = "62847acab40e6855a9b5905ccb99c2b5cf6b3ebb"
-uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
-version = "0.2.0"
-
-[[CUDAapi]]
-deps = ["Libdl", "Logging"]
-git-tree-sha1 = "831b825d10104bd29e28f6da93312a976830717b"
-uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
-version = "4.0.0"
-
-[[CUDAdrv]]
-deps = ["CEnum", "CUDAapi", "Printf"]
-git-tree-sha1 = "e650cbaee92b60433313157926b1e80d0c3a0e2e"
-uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
-version = "6.2.2"
-
-[[CUDAnative]]
-deps = ["Adapt", "BinaryProvider", "CEnum", "CUDAapi", "CUDAdrv", "Cthulhu", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "MacroTools", "Pkg", "Printf", "TimerOutputs"]
-git-tree-sha1 = "d1fc99635d0002c8a819b78cb1f441eb44310725"
-uuid = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
-version = "3.0.2"
-
-[[CodeTracking]]
-deps = ["InteractiveUtils", "UUIDs"]
-git-tree-sha1 = "0becdab7e6fbbcb7b88d8de5b72e5bb2f28239f3"
-uuid = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2"
-version = "0.5.8"
-
-[[CodecZlib]]
-deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "ded953804d019afa9a3f98981d99b33e3db7b6da"
-uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.0"
-
-[[ColorTypes]]
-deps = ["FixedPointNumbers", "Random"]
-git-tree-sha1 = "c4c1cca28748906265ed62c788d6fe6f0134d264"
-uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
-version = "0.10.0"
-
-[[Colors]]
-deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
-git-tree-sha1 = "2fdeb981ebcf52cd800ddb6a0aa5eac34153552d"
-uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
-version = "0.12.0"
-
-[[CommonSubexpressions]]
-deps = ["Test"]
-git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0"
-uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
-version = "0.2.0"
-
-[[CompilerSupportLibraries_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "7c4f882c41faa72118841185afc58a2eb00ef612"
-uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "0.3.3+0"
-
-[[Cthulhu]]
-deps = ["CodeTracking", "InteractiveUtils", "REPL", "Unicode"]
-git-tree-sha1 = "484790098c85c26f8e59051f8ff1a0745c034a7d"
-uuid = "f68482b8-f384-11e8-15f7-abe071a5a75f"
-version = "1.0.1"
-
-[[CuArrays]]
-deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "Libdl", "LinearAlgebra", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SparseArrays", "Statistics", "TimerOutputs"]
-git-tree-sha1 = "e8c55b38dcca955f5aed8ec4479cdc95810db1e1"
-uuid = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
-version = "2.0.1"
-
-[[DataAPI]]
-git-tree-sha1 = "674b67f344687a88310213ddfa8a2b3c76cc4252"
-uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.1.0"
-
-[[DataStructures]]
-deps = ["InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "73eb18320fe3ba58790c8b8f6f89420f0a622773"
-uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.17.11"
-
-[[Dates]]
-deps = ["Printf"]
-uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
-
-[[DelimitedFiles]]
-deps = ["Mmap"]
-uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-
-[[DiffResults]]
-deps = ["StaticArrays"]
-git-tree-sha1 = "da24935df8e0c6cf28de340b958f6aac88eaa0cc"
-uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
-version = "1.0.2"
-
-[[DiffRules]]
-deps = ["NaNMath", "Random", "SpecialFunctions"]
-git-tree-sha1 = "eb0c34204c8410888844ada5359ac8b96292cfd1"
-uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
-version = "1.0.1"
-
-[[Distributed]]
-deps = ["Random", "Serialization", "Sockets"]
-uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
-
-[[FillArrays]]
-deps = ["LinearAlgebra", "Random", "SparseArrays"]
-git-tree-sha1 = "51cc2f9bc4eb9c6c0e81ec2f779d1085583cc956"
-uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "0.8.7"
-
-[[FixedPointNumbers]]
-git-tree-sha1 = "3ba9ea634d4c8b289d590403b4a06f8e227a6238"
-uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
-version = "0.8.0"
-
-[[ForwardDiff]]
-deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
-git-tree-sha1 = "869540e4367122fbffaace383a5bdc34d6e5e5ac"
-uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "0.10.10"
-
-[[GPUArrays]]
-deps = ["AbstractFFTs", "Adapt", "LinearAlgebra", "Printf", "Random", "Serialization"]
-git-tree-sha1 = "d586762b08dcda13228df8967119b9cb6f22ade5"
-uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
-version = "3.1.0"
-
-[[IRTools]]
-deps = ["InteractiveUtils", "MacroTools", "Test"]
-git-tree-sha1 = "1a4355e4b5b50be2311ebb644f34f3306dbd0410"
-uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
-version = "0.3.1"
-
-[[InteractiveUtils]]
-deps = ["Markdown"]
-uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
-
-[[Juno]]
-deps = ["Base64", "Logging", "Media", "Profile"]
-git-tree-sha1 = "e1ba2a612645b3e07c773c3a208f215745081fe6"
-uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
-version = "0.8.1"
-
-[[LLVM]]
-deps = ["CEnum", "Libdl", "Printf", "Unicode"]
-git-tree-sha1 = "b6b86801ae2f2682e0a4889315dc76b68db2de71"
-uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
-version = "1.3.4"
-
-[[LibGit2]]
-deps = ["Printf"]
-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 = ["Markdown", "Random"]
-git-tree-sha1 = "f7d2e3f654af75f01ec49be82c231c382214223a"
-uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.5"
-
-[[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 = ["DataAPI"]
-git-tree-sha1 = "de0a5ce9e5289f27df672ffabef4d1e5861247d5"
-uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-version = "0.4.3"
-
-[[Mmap]]
-uuid = "a63ad114-7e13-5084-954f-fe012c677804"
-
-[[NNlib]]
-deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics"]
-git-tree-sha1 = "d9f196d911f55aeaff11b11f681b135980783824"
-uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.6.6"
-
-[[NaNMath]]
-git-tree-sha1 = "928b8ca9b2791081dc71a51c55347c27c618760f"
-uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
-version = "0.3.3"
-
-[[OpenSpecFun_jll]]
-deps = ["CompilerSupportLibraries_jll", "Libdl", "Pkg"]
-git-tree-sha1 = "d51c416559217d974a1113522d5919235ae67a87"
-uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
-version = "0.5.3+3"
-
-[[OrderedCollections]]
-deps = ["Random", "Serialization", "Test"]
-git-tree-sha1 = "c4c13474d23c60d20a67b217f1d7f22a40edf8f1"
-uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.1.0"
-
-[[Pkg]]
-deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
-uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-
-[[Printf]]
-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"
-
-[[Random]]
-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 = ["UUIDs"]
-git-tree-sha1 = "d37400976e98018ee840e0ca4f9d20baa231dc6b"
-uuid = "ae029012-a4dd-5104-9daa-d747884805df"
-version = "1.0.1"
-
-[[SHA]]
-uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
-
-[[Serialization]]
-uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
-
-[[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 = ["OpenSpecFun_jll"]
-git-tree-sha1 = "e19b98acb182567bcb7b75bb5d9eedf3a3b5ec6c"
-uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "0.10.0"
-
-[[StaticArrays]]
-deps = ["LinearAlgebra", "Random", "Statistics"]
-git-tree-sha1 = "5a3bcb6233adabde68ebc97be66e95dcb787424c"
-uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "0.12.1"
-
-[[Statistics]]
-deps = ["LinearAlgebra", "SparseArrays"]
-uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-
-[[StatsBase]]
-deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
-git-tree-sha1 = "a6102b1f364befdb05746f386b67c6b7e3262c45"
-uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.33.0"
-
-[[Test]]
-deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
-uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[[TimerOutputs]]
-deps = ["Printf"]
-git-tree-sha1 = "311765af81bbb48d7bad01fb016d9c328c6ede03"
-uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
-version = "0.5.3"
-
-[[TranscodingStreams]]
-deps = ["Random", "Test"]
-git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
-uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.9.5"
-
-[[UUIDs]]
-deps = ["Random", "SHA"]
-uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-
-[[Unicode]]
-uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
-
-[[ZipFile]]
-deps = ["Libdl", "Printf", "Zlib_jll"]
-git-tree-sha1 = "8748302cfdec02c4ae9c97b112cf10003f7f767f"
-uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.9.1"
-
-[[Zlib_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "2f6c3e15e20e036ee0a0965879b31442b7ec50fa"
-uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
-version = "1.2.11+9"
-
-[[Zygote]]
-deps = ["AbstractFFTs", "ArrayLayouts", "DiffRules", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "1ccbfbe8930376e31752b812daa2532c723dc332"
-uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.4.13"
-
-[[ZygoteRules]]
-deps = ["MacroTools"]
-git-tree-sha1 = "b3b4882cc9accf6731a08cc39543fbc6b669dca8"
-uuid = "700de1a5-db45-46bc-99cf-38207098b444"
-version = "0.2.0"

Project.toml

@@ -1,6 +1,6 @@
 name = "Flux"
 uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-version = "0.10.4"
+version = "0.11.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

src/data/Data.jl

@@ -51,4 +51,6 @@ export Iris
 include("housing.jl")
 export Housing
 
+@deprecate DataLoader(x...; kws...) DataLoader(x; kws...)
+
 end

src/data/dataloader.jl

@@ -1,7 +1,7 @@
 # Adapted from Knet's src/data.jl (author: Deniz Yuret)
 
-struct DataLoader
-    data
+struct DataLoader{D}
+    data::D
     batchsize::Int
     nobs::Int
     partial::Bool
@@ -11,21 +11,20 @@ struct DataLoader
 end
 
 """
-    DataLoader(data...; batchsize=1, shuffle=false, partial=true)
+    DataLoader(data; batchsize=1, shuffle=false, partial=true)
 
 An object that iterates over mini-batches of `data`, each mini-batch containing `batchsize` observations
 (except possibly the last one). 
 
-Takes as input one or more data tensors, e.g. X in unsupervised learning, X and Y in 
-supervised learning. The last dimension in each tensor is considered to be the observation
-dimension. 
+Takes as input a data tensors or a tuple of one or more such tensors. 
+The last dimension in each tensor is considered to be the observation dimension. 
 
 If `shuffle=true`, shuffles the observations each time iterations are re-started.
 If `partial=false`, drops the last mini-batch if it is smaller than the batchsize.
 
-The original data is preserved as a tuple in the `data` field of the DataLoader. 
+The original data is preserved in the `data` field of the DataLoader. 
 
-Example usage:
+Usage example:
 
     Xtrain = rand(10, 100)
     train_loader = DataLoader(Xtrain, batchsize=2) 
@@ -37,9 +36,16 @@ Example usage:
 
     train_loader.data   # original dataset
 
+    # similar, but yielding tuples
+    train_loader = DataLoader((Xtrain,), batchsize=2) 
+    for (x,) in train_loader
+        @assert size(x) == (10, 2)
+        ...
+    end
+
     Xtrain = rand(10, 100)
     Ytrain = rand(100)
-    train_loader = DataLoader(Xtrain, Ytrain, batchsize=2, shuffle=true) 
+    train_loader = DataLoader((Xtrain, Ytrain), batchsize=2, shuffle=true) 
     for epoch in 1:100
         for (x, y) in train_loader
             @assert size(x) == (10, 2)
@@ -52,25 +58,19 @@ Example usage:
     using IterTools: ncycle 
     Flux.train!(loss, ps, ncycle(train_loader, 10), opt)
 """
-function DataLoader(data...; batchsize=1, shuffle=false, partial=true)
-    length(data) > 0 || throw(ArgumentError("Need at least one data input"))
+function DataLoader(data; batchsize=1, shuffle=false, partial=true)
     batchsize > 0 || throw(ArgumentError("Need positive batchsize"))
     
-    nx = size(data[1])[end]
-    for i=2:length(data)
-        nx != size(data[i])[end] && throw(DimensionMismatch("All data should contain same number of observations"))
+    n = _nobs(data) 
+    if n < batchsize
+        @warn "Number of observations less than batchsize, decreasing the batchsize to $n"
+        batchsize = n
     end
-    if nx < batchsize
-        @warn "Number of data points less than batchsize, decreasing the batchsize to $nx"
-        batchsize = nx
-    end
-    imax = partial ? nx : nx - batchsize + 1
-    ids = 1:min(nx, batchsize)
-    DataLoader(data, batchsize, nx, partial, imax, [1:nx;], shuffle)
+    imax = partial ? n : n - batchsize + 1
+    ids = 1:min(n, batchsize)
+    DataLoader(data, batchsize, n, partial, imax, [1:n;], shuffle)
 end
 
-getdata(x::AbstractArray, ids) = x[(Base.Colon() for _=1:ndims(x)-1)..., ids]
-
 @propagate_inbounds function Base.iterate(d::DataLoader, i=0)     # returns data in d.indices[i+1:i+batchsize]
     i >= d.imax && return nothing
     if d.shuffle && i == 0
@@ -78,11 +78,7 @@ getdata(x::AbstractArray, ids) = x[(Base.Colon() for _=1:ndims(x)-1)..., ids]
     end
     nexti = min(i + d.batchsize, d.nobs)
     ids = d.indices[i+1:nexti]
-    if length(d.data) == 1
-        batch = getdata(d.data[1], ids)
-    else
-        batch = ((getdata(x, ids) for x in d.data)...,)
-    end
+    batch = _getobs(d.data, ids)
     return (batch, nexti)
 end
 
@@ -90,3 +86,22 @@ function Base.length(d::DataLoader)
     n = d.nobs / d.batchsize
     d.partial ? ceil(Int,n) : floor(Int,n)
 end
+
+_nobs(data::AbstractArray) = size(data)[end]
+
+function _nobs(data::Tuple)
+    length(data) > 0 || throw(ArgumentError("Need at least one data input"))
+    n = _nobs(data[1])
+    if !all(x -> _nobs(x) == n, data[2:end])
+        throw(DimensionMismatch("All data should contain same number of observations"))
+    end
+    return n
+end
+
+function _getobs(data::A, i) where A<:AbstractArray{T,N} where {T,N}
+    getindex(data, ntuple(i->Colon(), N-1)..., i)
+end
+
+_getobs(data::Tuple, i) = ((_getobs(x, i) for x in data)...,)
+
+Base.eltype(d::DataLoader{D}) where D = D

src/optimise/train.jl

@@ -56,14 +56,17 @@ function stop()
   throw(StopException())
 end
 
+maketuple(x) = (x,)
+maketuple(x::Tuple) = x
+
 """
     train!(loss, params, data, opt; cb)
 
-For each datapoint `d` in `data` compute the gradient of `loss(d...)` through
-backpropagation and call the optimizer `opt`.
+For each datapoint `d` in `data`, assumed to be a tuple, compute the gradient of `loss(d...)` 
+with respect to `params`,  and call the optimizer `opt`.
 
-In case datapoints `d` are of numeric array type, assume no splatting is needed
-and compute the gradient of `loss(d)`.
+If `data` yields a tuple mini-batch `d` under iteration, it will be splatted in the function call
+`loss(d...)`, otherwise `loss(d)` will be called for non-tuple mini-batches.
 
 A callback is given with the keyword argument `cb`. For example, this will print
 "training" every 10 seconds (using [`Flux.throttle`](@ref)):
@@ -80,14 +83,8 @@ function train!(loss, ps, data, opt; cb = () -> ())
   cb = runall(cb)
   @progress for d in data
     try
-      if d isa AbstractArray{<:Number}
-        gs = gradient(ps) do
-          loss(d)
-        end
-      else
-        gs = gradient(ps) do
-          loss(d...)
-        end
+      gs = gradient(ps) do
+          loss(maketuple(d)...)
       end
       update!(opt, ps, gs)
       cb()

test/data.jl

@@ -4,6 +4,7 @@
 
     d = DataLoader(X, batchsize=2)
     batches = collect(d)
+    @test eltype(batches) == eltype(d) == typeof(X)
     @test length(batches) == 3
     @test batches[1] == X[:,1:2]
     @test batches[2] == X[:,3:4]
@@ -11,12 +12,21 @@
 
     d = DataLoader(X, batchsize=2, partial=false)
     batches = collect(d)
+    @test eltype(batches) == eltype(d) == typeof(X)
     @test length(batches) == 2
     @test batches[1] == X[:,1:2]
     @test batches[2] == X[:,3:4]
 
-    d = DataLoader(X, Y, batchsize=2)
+    d = DataLoader((X,), batchsize=2, partial=false)
     batches = collect(d)
+    @test eltype(batches) == eltype(d) == Tuple{typeof(X)}
+    @test length(batches) == 2
+    @test batches[1] == (X[:,1:2],)
+    @test batches[2] == (X[:,3:4],)
+
+    d = DataLoader((X, Y), batchsize=2)
+    batches = collect(d)
+    @test eltype(batches) == eltype(d) == Tuple{typeof(X), typeof(Y)}
     @test length(batches) == 3
     @test length(batches[1]) == 2
     @test length(batches[2]) == 2
@@ -41,7 +51,7 @@
     X = ones(2, 10)
     Y = fill(2, 10)
     loss(x, y) = sum((y - x'*θ).^2)
-    d  = DataLoader(X, Y) 
+    d  = DataLoader((X, Y)) 
     Flux.train!(loss, [θ], ncycle(d, 10), Descent(0.1))
     @test norm(θ .- 1) < 1e-10
 end

test/runtests.jl

@@ -2,49 +2,45 @@ using Flux
 using Flux.Data
 using Test 
 using Random, Statistics, LinearAlgebra
-using Documenter
 using IterTools: ncycle
 
 Random.seed!(0)
 
-@testset "Flux" begin
+@testset "Utils" begin
+  include("utils.jl")
+end
 
-  @testset "Utils" begin
-    include("utils.jl")
-  end
-
-  @testset "Onehot" begin
-    include("onehot.jl")
-  end
-
-  @testset "Optimise" begin
-    include("optimise.jl")
-  end
-
-  @testset "Data" begin
-    include("data.jl")
-  end
-
-  @testset "Layers" begin
-    include("layers/basic.jl")
-    include("layers/normalisation.jl")
-    include("layers/stateless.jl")
-    include("layers/conv.jl")
-  end
-
-  @testset "CUDA" begin
-    if Flux.use_cuda[]
-      include("cuda/cuda.jl")
-    else
-      @warn "CUDA unavailable, not testing GPU support"
-    end
+@testset "Onehot" begin
+  include("onehot.jl")
+end
+
+@testset "Optimise" begin
+  include("optimise.jl")
+end
+
+@testset "Data" begin
+  include("data.jl")
+end
+
+@testset "Layers" begin
+  include("layers/basic.jl")
+  include("layers/normalisation.jl")
+  include("layers/stateless.jl")
+  include("layers/conv.jl")
+end
+
+@testset "CUDA" begin
+  if Flux.use_cuda[]
+    include("cuda/cuda.jl")
+  else
+    @warn "CUDA unavailable, not testing GPU support"
   end
+end
 
+@static if VERSION >= v"1.4"
+  using Documenter
   @testset "Docs" begin
-    if VERSION >= v"1.4"
-      DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive=true)
-      doctest(Flux)
-    end
+    DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive=true)
+    doctest(Flux)
   end
-
-end # testset Flux
+end