add L1 regularisation to WeightDecay

Project.toml

@@ -44,7 +44,7 @@ MacroTools = "0.5"
 Metal = "0.5"
 NNlib = "0.9.1"
 OneHotArrays = "0.2.4"
-Optimisers = "0.2.12, 0.3.0"
+Optimisers = "0.3.2"
 Preferences = "1"
 ProgressLogging = "0.1"
 Reexport = "1.0"

src/deprecations.jl

@@ -126,9 +126,9 @@ for T in [:Descent, :Adam, :Momentum, :Nesterov,
 end
 _old_to_new(rule::Optimiser) = Optimisers.OptimiserChain(map(_old_to_new, rule.os)...)
 const OptimiserChain = Optimise.Optimiser  # lets you use new name with implicit params too.
-_old_to_new(rule::WeightDecay) = Optimisers.WeightDecay(rule.wd)  # called gamma now
 _old_to_new(rule::ClipNorm) = Optimisers.ClipNorm(rule.thresh)  # called omega, and there are more fields 
 _old_to_new(rule::ClipValue) = Optimisers.ClipGrad(rule.thresh)  # called delta now, and struct name differs
+_old_to_new(rule::WeightDecay) = Optimisers.WeightDecay(rule.wd, rule.alpha)  # wd is called lambda now
 const ClipGrad = Optimise.ClipValue
 _old_to_new(rule::RMSProp) = Optimisers.RMSProp(rule.eta, rule.rho, rule.epsilon)  # RMSProp has no field centred
 

src/optimise/optimisers.jl

@@ -681,6 +681,12 @@ Typically composed  with other optimisers as the first transformation to the gra
 making it equivalent to adding ``L_2`` regularization 
 with coefficient  ``λ`` to the loss.
 
+    WeightDecay(λ, α)
+
+Second argument turns on ``L_1`` regularization, adding `λ * (α * sign(x) + (1 - α) * x)`
+to the gradient. The case `α = 1` is equivalent to adding `sum(abs, x) == norm(x, 1)` to the 
+loss function, while `0 < α < 1` mixes L1 and L2.
+
 # Examples
 
 ```julia
@@ -689,13 +695,16 @@ opt = Optimiser(WeightDecay(1f-4), Adam())
 """
 mutable struct WeightDecay <: AbstractOptimiser
   wd::Real
+  alpha::Real
 end
 
-WeightDecay() = WeightDecay(0)
+WeightDecay(λ = 0f0) = WeightDecay(λ, 0f0)
 
 function apply!(o::WeightDecay, x, Δ)
-  wd = o.wd
-  @. Δ += wd * x
+  wd, α = o.wd, o.alpha
+  l2 = (1-α) * wd
+  l1 = α * wd
+  @. Δ += l2 * x + l1 * sign(x)
 end
 
 """