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
g = zeros(grads) # Creates a vector of length sum(length, grads)
copyto!(g,grads) # Store grads in vector g
copyto!(grads,g) # 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.