reorganise

docs/src/models/layers.md

@@ -30,3 +30,11 @@ leakyrelu
 elu
 swish
 ```
+
+## Normalisation & Regularisation
+
+These layers don't affect the structure of the network but may improve training times or reduce overfitting.
+
+```@docs
+Dropout
+```

src/Flux.jl

@@ -9,7 +9,7 @@ using Lazy: @forward
 
 export Chain, Dense, RNN, LSTM, Dropout,
   SGD, ADAM, Momentum, Nesterov,
-  param, params, mapleaves, testmode!
+  param, params, mapleaves
 
 using NNlib
 export σ, relu, leakyrelu, elu, swish, softmax
@@ -27,5 +27,6 @@ include("tree.jl")
 include("layers/stateless.jl")
 include("layers/basic.jl")
 include("layers/recurrent.jl")
+include("layers/normalisation.jl")
 
 end # module

src/layers/basic.jl

@@ -78,47 +78,3 @@ function Base.show(io::IO, l::Dense)
   l.σ == identity || print(io, ", ", l.σ)
   print(io, ")")
 end
-
-
-"""
-  Dropout(p; testmode=false)
-
-A Dropout layer. If `testmode=false` mode sets input components `x[i]` to zero with
-probability `p` and to `x[i]/(1-p)` with probability `(1-p)`.
-
-In `testmode=true`it doesn't alter the input: `x == Dropout(p; mode=:eval)(x)`.
-Change the mode with [`testmode!`](@ref).
-"""
-mutable struct Dropout{F}
-  p::F
-  testmode::Bool
-end
-Dropout(p::F; testmode::Bool=false) where {F} = Dropout{F}(p, testmode)
-
-function (a::Dropout)(x)
-  if a.testmode
-    return x
-  else
-    if 0 < a.p < 1
-      y = similar(x)
-      rand!(y)
-      q = 1 - a.p
-      @inbounds for i=1:length(y)
-        y[i] = y[i] > a.p ? 1 / q : 0
-      end
-      return y .* x
-    elseif a.p == 0
-      return x
-    elseif a.p == 1
-      return zeros(x)
-    end
-  end
-end
-
-"""
-    testmode!(m, val=true)
-
-Set model `m` in test mode if `val=true`, and in training mode otherwise.
-This has an affect only if `m` contains [`Dropout`](@ref) or `BatchNorm` layers.
-"""
-testmode!(m, val::Bool=true) = prefor(x -> :testmode ∈ fieldnames(x) && (x.testmode = val), m)

src/layers/normalisation.jl

@@ -0,0 +1,45 @@
+"""
+    testmode!(m)
+    testmode!(m, false)
+
+Put layers like [`Dropout`](@ref) and `BatchNorm` into testing mode (or back to
+training mode with `false`).
+"""
+function testmode!(m, val::Bool=true)
+  prefor(x -> _testmode!(x, val), m)
+  return m
+end
+
+_testmode!(m, test) = nothing
+
+"""
+    Dropout(p)
+
+A Dropout layer. For each input, either sets that input to `0` (with probability
+`p`) or scales it by `1/(1-p)`. This is used as a regularisation, i.e. it
+reduces overfitting during training.
+
+Does nothing to the input once in [`testmode!`](@ref).
+"""
+mutable struct Dropout{F}
+  p::F
+  active::Bool
+end
+
+function Dropout(p)
+  @assert 0 ≤ p ≤ 1
+  Dropout{typeof(p)}(p, true)
+end
+
+function (a::Dropout)(x)
+  a.active || return x
+  y = similar(x)
+  rand!(y)
+  q = 1 - a.p
+  @inbounds for i=1:length(y)
+    y[i] = y[i] > a.p ? 1 / q : 0
+  end
+  return y .* x
+end
+
+_testmode!(a::Dropout, test) = (a.active = !test)

test/layers/normalisation.jl

@@ -1,8 +1,10 @@
-@testset "dropout" begin
+using Flux: testmode!
+
+@testset "Dropout" begin
   x = [1.,2.,3.]
-  @test x === Dropout(0.1, testmode=true)(x)
-  @test x === Dropout(0, testmode=false)(x)
-  @test all(zeros(x) .== Dropout(1, testmode=false)(x))
+  @test x == testmode!(Dropout(0.1))(x)
+  @test x == Dropout(0)(x)
+  @test zeros(x) == Dropout(1)(x)
 
   x = rand(100)
   m = Dropout(0.9)

test/runtests.jl

@@ -4,6 +4,6 @@ using Flux, Base.Test
 
 include("utils.jl")
 include("tracker.jl")
-include("layers.jl")
+include("layers/normalisation.jl")
 
 end