update docs and export update!

docs/src/training/optimisers.md

@@ -21,7 +21,7 @@ grads = gradient(() -> loss(x, y), θ)
 We want to update each parameter, using the gradient, in order to improve (reduce) the loss. Here's one way to do that:
 
 ```julia
-using Flux: update!
+using Flux.Optimise: update!
 
 η = 0.1 # Learning Rate
 for p in (W, b)
@@ -46,6 +46,7 @@ An optimiser `update!` accepts a parameter and a gradient, and updates the param
 All optimisers return an object that, when passed to `train!`, will update the parameters passed to it.
 
 ```@docs
+Flux.Optimise.update!
 Descent
 Momentum
 Nesterov

src/optimise/Optimise.jl

@@ -1,6 +1,6 @@
 module Optimise
 
-export train!,
+export train!, update!,
 	SGD, Descent, ADAM, Momentum, Nesterov, RMSProp,
 	ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM, ADAMW,RADAM, 
 	InvDecay, ExpDecay, WeightDecay, stop, Optimiser

src/optimise/train.jl

@@ -1,9 +1,22 @@
 using Juno
 import Zygote: Params, gradient
 
+
+"""
+  update!(opt, p, g)
+  update!(opt, ps::Params, gs)
+
+Perform an update step of the parameters `ps` (or the single parameter `p`) 
+according to optimizer `opt`  and the gradients `gs` (the gradient `g`).
+
+As a result, the parameters are mutated and the optimizer's internal state may change. 
+
+  update!(x, x̄)
+  
+Update the array `x` according to `x .-= x̄`.
+"""
 function update!(x::AbstractArray, x̄)
-  x .+= x̄
+  x .-= x̄
-  return x
 end
 
 function update!(opt, x, x̄)