docs update

Manifest.toml

@@ -18,6 +18,12 @@ git-tree-sha1 = "c88cfc7f9c1f9f8633cddf0b56e86302b70f64c5"
 uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 version = "1.0.1"
 
+[[ArrayLayouts]]
+deps = ["FillArrays", "LinearAlgebra"]
+git-tree-sha1 = "bc779df8d73be70e4e05a63727d3a4dfb4c52b1f"
+uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
+version = "0.1.5"
+
 [[Base64]]
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 
@@ -230,9 +236,9 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[NNlib]]
 deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics"]
-git-tree-sha1 = "21a3c22bc197b6ae2f8d4d75631876e2b6506dbe"
+git-tree-sha1 = "d9f196d911f55aeaff11b11f681b135980783824"
 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.6.5"
+version = "0.6.6"
 
 [[NaNMath]]
 git-tree-sha1 = "928b8ca9b2791081dc71a51c55347c27c618760f"
@@ -360,10 +366,10 @@ uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
 version = "1.2.11+8"
 
 [[Zygote]]
-deps = ["DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "f8329b595c465caf3ca87c4f744e6041a4983e43"
+deps = ["ArrayLayouts", "DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
+git-tree-sha1 = "7dc5fdb4917ac5a84e199ae654316a01cd4a278b"
 uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.4.8"
+version = "0.4.9"
 
 [[ZygoteRules]]
 deps = ["MacroTools"]

docs/src/models/nnlib.md

@@ -12,9 +12,9 @@ NNlib.gelu
 NNlib.leakyrelu
 NNlib.logcosh
 NNlib.logsigmoid
-NNlib.sigmoid
 NNlib.relu
 NNlib.selu
+NNlib.sigmoid
 NNlib.softplus
 NNlib.softsign
 NNlib.swish
@@ -47,4 +47,5 @@ NNlib.depthwiseconv
 NNlib.batched_mul
 NNlib.batched_mul!
 NNlib.batched_adjoint
+NNlib.batched_transpose
 ```

docs/src/performance.md

@@ -4,7 +4,7 @@ All the usual [Julia performance tips apply](https://docs.julialang.org/en/v1/ma
 As always [profiling your code](https://docs.julialang.org/en/v1/manual/profile/#Profiling-1) is generally a useful way of finding bottlenecks.
 Below follow some Flux specific tips/reminders.
 
-## Don't use more precision than you need.
+## Don't use more precision than you need
 
 Flux works great with all kinds of number types.
 But often you do not need to be working with say `Float64` (let alone `BigFloat`).
@@ -14,7 +14,8 @@ Which means allocations occur much faster.
 And you use less memory.
 
 
-## Make sure your activation and loss functions preserve the type of their inputs
+## Preserve inputs' types
+
 Not only should your activation and loss functions be [type-stable](https://docs.julialang.org/en/v1/manual/performance-tips/#Write-%22type-stable%22-functions-1),
 they should also preserve the type of their inputs.
 
@@ -29,24 +30,22 @@ because it results in having to use slow mixed type multiplication in the dense
 Similar situations can occur in the loss function during backpropagation.
 
 Which means if you change your data say from `Float64` to `Float32` (which should give a speedup: see above),
-you will see a large slow-down
+you will see a large slow-down.
 
 This can occur sneakily, because you can cause type-promotion by interacting with a numeric literals.
 E.g. the following will have run into the same problem as above:
 
 ```
-    leaky_tanh(x) = 0.01x + tanh(x)
+    leaky_tanh(x) = 0.01*x + tanh(x)
 ```
 
-While one could change your activation function (e.g. to use `0.01f0x`) to avoid this when ever your inputs change,
-the idiomatic (and safe way) is to use `oftype`.
-
+While one could change the activation function (e.g. to use `0.01f0x`), the idiomatic (and safe way)  to avoid type casts whenever inputs changes is to use `oftype`:
 ```
-    leaky_tanh(x) = oftype(x/1, 0.01)x + tanh(x)
+    leaky_tanh(x) = oftype(x/1, 0.01)*x + tanh(x)
 ```
 
 
-## Evaluate batches as Matrices of features, rather than sequences of Vector features
+## Evaluate batches as Matrices of features
 
 While it can sometimes be tempting to process your observations (feature vectors) one at a time
 e.g.

src/data/dataloader.jl

@@ -23,21 +23,25 @@ dimension.
 If `shuffle=true`, shuffles the observations each time iterations are re-started.
 If `partial=false`, drops the last mini-batch if it is smaller than the batchsize.
 
+The original data is preserved as a tuple in the `data` field of the DataLoader. 
+
 Example usage:
 
     Xtrain = rand(10, 100)
-    dtrain = DataLoader(Xtrain, batchsize=2) 
-    # iterate over 50 mini-batches
-    for x in dtrain: 
+    train_loader = DataLoader(Xtrain, batchsize=2) 
+    # iterate over 50 mini-batches of size 2
+    for x in train_loader: 
         @assert size(x) == (10, 2)
         ...
     end
 
+    train_loader.data   # original dataset
+
     Xtrain = rand(10, 100)
     Ytrain = rand(100)
-    dtrain = 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 dtrain: 
+        for (x, y) in train_loader: 
             @assert size(x) == (10, 2)
             @assert size(y) == (2,)
             ...
@@ -46,7 +50,7 @@ Example usage:
 
     # train for 10 epochs
     using IterTools: ncycle 
-    Flux.train!(loss, ps, ncycle(dtrain, 10), opt)
+    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"))