extend update! with an optimiser

docs/src/training/optimisers.md

@@ -3,7 +3,7 @@
 Consider a [simple linear regression](../models/basics.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.
 
 ```julia
-using Flux.Tracker
+using Flux, Flux.Tracker
 
 W = param(rand(2, 5))
 b = param(rand(2))
@@ -14,8 +14,8 @@ loss(x, y) = sum((predict(x) .- y).^2)
 x, y = rand(5), rand(2) # Dummy data
 l = loss(x, y) # ~ 3
 
-params = Params([W, b])
-grads = Tracker.gradient(() -> loss(x, y), params)
+θ = Params([W, b])
+grads = Tracker.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:
@@ -35,7 +35,7 @@ Running this will alter the parameters `W` and `b` and our loss should go down.
 opt = Descent(0.1) # Gradient descent with learning rate 0.1
 
 for p in (W, b)
-  update!(opt, p, -η * grads[p])
+  update!(opt, p, grads[p])
 end
 ```
 

src/optimise/train.jl

@@ -1,7 +1,11 @@
 using Juno
-using Flux.Tracker: data, grad, back!
+import Flux.Tracker: data, grad, back!, update!
 import Base.depwarn
 
+function update!(opt, x, x̄)
+  update!(x, apply!(opt, x, copy(data(x̄))))
+end
+
 function _update_params!(opt, xs)
   for x in xs
     Δ = apply!(opt, x.data, x.grad)