# [ANN] FluxOptTools

FluxOptTools.jl
This package contains some utilities to enhance training of Flux.jl models.

## Train using Optim

Optim.jl can be used to train Flux models (if Flux is on branch `sf/zygote_updated`), here’s an example how

``````using Flux, Zygote, Optim, FluxOptTools, Statistics
m      = Chain(Dense(1,3,tanh) , Dense(3,1))
x      = LinRange(-pi,pi,100)'
y      = sin.(x)
loss() = mean(abs2, m(x) .- y)
Zygote.refresh()
pars   = Flux.params(m)
lossfun, gradfun, fg!, p0 = optfuns(loss, pars)
res = Optim.optimize(Optim.only_fg!(fg!), p0, Optim.Options(iterations=1000, store_trace=true))
``````

The utility provided by this package is the function `optfuns` which returns three functions and `p0`, a vectorized version of `pars`. L-BFGS typically has better convergence properties than, e.g., the ADAM optimizer. Here’s a benchmark where L-BFGS in red beats ADAM with tuned step size in blue.

The code for this benchmark is in the `runtests.jl`.

## Visualize loss landscape

We define a plot recipe such that a loss landscape can be plotted with

``````using Plots
plot(loss, pars, l=0.1, npoints=50, seriestype=:contour)
``````

The landscape is plotted by selecting two random directions and extending the current point (`pars`) a distance `l*norm(pars)` (both negative and positive) along the two random directions. The number of loss evaluations will be `npoints^2`.

## Flatten and Unflatten

What this package really does is flattening and reassembling the types `Flux.Params` and `Zygote.Grads` to and from vectors. These functions are used like so

``````p = zeros(pars)  # Creates a vector of length sum(length, pars)
copyto!(p,pars)  # Store pars in vector p
copyto!(pars,p)  # Reverse

This is what is used under the hood in the functions returned from `optfuns` in order to have everything on a form that Optim understands.