Flux.jl/src/optimise/train.jl

using Juno
using Flux.Tracker: back!

tocb(f) = f
tocb(fs::AbstractVector) = () -> foreach(call, fs)

function train!(loss, data, opt; cb = () -> ())
  cb = tocb(cb)
  @progress for x in data
    l = loss(x...)
    isinf(l.data[]) && error("Loss is Inf")
    isnan(l.data[]) && error("Loss is NaN")
    back!(l)
    opt()
    cb()
  end
end
juno progress 2017-09-03 06:44:32 +00:00			`using Juno`
opt refactor 2017-08-31 16:36:18 +00:00			`using Flux.Tracker: back!`

cb convenience 2017-09-07 04:27:16 +00:00			`tocb(f) = f`
			`tocb(fs::AbstractVector) = () -> foreach(call, fs)`

change argument name for better clarity 2017-10-11 11:18:50 +00:00			`function train!(loss, data, opt; cb = () -> ())`
cb convenience 2017-09-07 04:27:16 +00:00			`cb = tocb(cb)`
more flexible training loop 2017-09-11 12:11:55 +00:00			`@progress for x in data`
change argument name for better clarity 2017-10-11 11:18:50 +00:00			`l = loss(x...)`
more informative 2017-09-27 17:33:23 +00:00			`isinf(l.data[]) && error("Loss is Inf")`
			`isnan(l.data[]) && error("Loss is NaN")`
tweaks 2017-09-12 13:11:03 +00:00			`back!(l)`
handle epoch elsewhere 2017-09-07 04:29:55 +00:00			`opt()`
			`cb()`
basic training loop 2017-08-24 10:42:29 +00:00			`end`
			`end`