Merge #1612

1612: fix AdamW and improve decays docs r=DhairyaLGandhi a=CarloLucibello

There is great disorder under the sky with optimizers. Since in chaining optimizers 
```
opt = Optimizer(opt1, opt2)
```
the order generally matters (a lot!) we have to be very careful in documenting how to use decays. In fact, we were giving completely wrong indirections for `InvDecays` and `ExpDecays`. The correct ordering for standard use is

```julia
Optimizer(WeightDecay(), ADAM())   # equivalent to L2 regularization
Optimizer(ADAM(), InvDecay())   # learning rate scheduling
Optimizer(ADAM(), ExpDecay())   # learning rate scheduling
```
Different orderings are to be typically considered as bugs in user code. 

This PR fixes examples and tries to clarify documentation in this regard. 

Also fixes AdamW, which was doing something totally wrong due to the aforementioned confusion. 
(see https://towardsdatascience.com/why-adamw-matters-736223f31b5d for how AdamW works).

Related in model-zoo: FluxML/model-zoo#303 and FluxML/model-zoo#304




Co-authored-by: CarloLucibello <carlo.lucibello@gmail.com>
Co-authored-by: Carlo Lucibello <carlo.lucibello@unibocconi.it>

Author: bors[bot]

src/optimise/optimisers.jl

@@ -491,7 +491,7 @@ opt = ADAMW(0.001, (0.89, 0.995), 0.1)
 ```
 """
 ADAMW(η = 0.001, β = (0.9, 0.999), decay = 0) =
-  Optimiser(ADAM(η, β), WeightDecay(decay))
+  Optimiser(ADAM(1, β), WeightDecay(decay), Descent(η))
 
 """
     AdaBelief(η = 0.001, β::Tuple = (0.9, 0.999))
@@ -564,9 +564,18 @@ Apply inverse time decay to an optimiser, so that the effective step size at
 iteration `n` is `eta / (1 + γ * n)` where `eta` is the initial step size.
 The wrapped optimiser's step size is not modified.
 
+See also the [Scheduling Optimisers](@ref) section of the docs
+for more general scheduling techniques.
+
 # Examples
+
+`InvDecay` is typically composed  with other optimizers 
+as the last transformation of the gradient:
+
 ```julia
-Optimiser(InvDecay(..), Opt(..))
+# Inverse decay of the learning rate
+# with starting value 0.001 and decay coefficient 0.01.
+opt = Optimiser(Adam(1f-3), InvDecay(1f-2))
 ```
 """
 mutable struct InvDecay <: AbstractOptimiser
@@ -598,12 +607,16 @@ a minimum of `clip`.
                 two decay operations.
 - `clip`: Minimum value of learning rate.
 
+
+See also the [Scheduling Optimisers](@ref) section of the docs
+for more general scheduling techniques.
+
 # Examples
-To apply exponential decay to an optimiser:
-```julia
-Optimiser(ExpDecay(..), Opt(..))
 
-opt = Optimiser(ExpDecay(), ADAM())
+`ExpDecay` is typically composed  with other optimizers 
+as the last transformation of the gradient:
+```julia
+opt = Optimiser(ADAM(), ExpDecay())
 ```
 """
 mutable struct ExpDecay <: AbstractOptimiser
@@ -614,7 +627,8 @@ mutable struct ExpDecay <: AbstractOptimiser
   current::IdDict
 end
 
-ExpDecay(opt = 0.001, decay = 0.1, decay_step = 1000, clip = 1e-4) = ExpDecay(opt, decay, decay_step, clip, IdDict())
+ExpDecay(opt = 0.001, decay = 0.1, decay_step = 1000, clip = 1e-4) = 
+  ExpDecay(opt, decay, decay_step, clip, IdDict())
 
 function apply!(o::ExpDecay, x, Δ)
   η, s, decay = o.eta, o.step, o.decay
@@ -627,12 +641,18 @@ function apply!(o::ExpDecay, x, Δ)
 end
 
 """
-    WeightDecay(wd = 0)
+    WeightDecay(λ = 0)
 
-Decay weights by `wd`.
+Decay weights by ``λ``. 
+Typically composed  with other optimizers as the first transformation to the gradient,
+making it equivalent to adding ``L_2`` regularization 
+with coefficient  ``λ`` to the loss.
 
-# Parameters
-- Weight decay (`wd`)
+# Examples
+
+```julia
+opt = Optimiser(WeigthDecay(1f-4), ADAM())
+```
 """
 mutable struct WeightDecay <: AbstractOptimiser
   wd::Real