add logsigmoid

src/NNlib.jl

@@ -2,13 +2,14 @@ module NNlib
 
 using Requires
 
-export σ, sigmoid, relu, leakyrelu, elu, swish, selu, softplus, softsign,
+export σ, sigmoid, relu, leakyrelu, elu, swish, selu, softplus, softsign, logσ, logsigmoid,
   softmax, logsoftmax, conv2d, maxpool2d, avgpool2d
 
 const libnnlib = Libdl.find_library("nnlib.$(Libdl.dlext)", [joinpath(@__DIR__, "..", "deps")])
 
 include("numeric.jl")
 include("activation.jl")
+include("logsigmoid.jl")
 include("softmax.jl")
 include("logsoftmax.jl")
 include("linalg.jl")

src/logsigmoid.jl

@@ -0,0 +1,24 @@
+
+"""
+    logσ(x)
+
+Return `log(σ(x))` which is computed in a numerically stable way.
+
+    julia> logσ(0.)
+    -0.6931471805599453
+    julia> logσ.([-100, -10, 100.])
+    3-element Array{Float64,1}:
+     -100.0
+      -10.0
+       -0.0
+"""
+function logσ(x)
+  max_v = max(zero(x), -x)
+  z = exp(-max_v) + exp(-x-max_v)
+  -(max_v + log(z))
+end
+
+∇logσ(Δ, x) = Δ * (1 - σ(x))
+
+const logsigmoid = logσ
+const ∇logsigmoid = ∇logσ

test/runtests.jl

@@ -9,10 +9,16 @@ include("conv.jl")
 xs = rand(5)
 @test softmax(xs) ≈ exp.(xs) ./ sum(exp.(xs))
 @test logsoftmax(xs) ≈ log.(softmax(xs))
+@test logsigmoid.(xs) ≈ log.(sigmoid.(xs))
 
 xs = rand(5,10)
 @test softmax(xs) ≈ exp.(xs) ./ sum(exp.(xs),1)
 @test logsoftmax(xs) ≈ log.(softmax(xs))
+@test logsigmoid.(xs) ≈ log.(sigmoid.(xs))
+
+for T in [:Float32, :Float64]
+  @eval @test logsigmoid.($T[-100_000, 100_000.]) ≈ $T[-100_000, 0.]
+end
 
 ## compare the outputs with the PyTorch nn.LogSoftmax returns
 xs = Float32[1, 2, 3000.]