107 lines
2.1 KiB
Julia
107 lines
2.1 KiB
Julia
using Juno
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using Flux.Tracker: data, grad, back!
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import Base.depwarn
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function update!(opt, xs)
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for x in xs
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Δ = update!(opt, x.data, x.grad)
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x.data .-= Δ
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Δ .= 0
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end
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end
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# Callback niceties
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call(f, xs...) = f(xs...)
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runall(f) = f
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runall(fs::AbstractVector) = () -> foreach(call, fs)
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# The AD generates fairly large backtraces that are unhelpful if you interrupt
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# while training; this just cleans that up.
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macro interrupts(ex)
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:(try $(esc(ex))
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catch e
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e isa InterruptException || rethrow()
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throw(e)
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end)
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end
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struct StopException <: Exception end
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"""
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stop()
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Call `Flux.stop()` in a callback to indicate when a callback condition is met.
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This would trigger the train loop to stop and exit.
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```julia
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# Example callback:
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cb = function ()
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accuracy() > 0.9 && Flux.stop()
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end
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```
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"""
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function stop()
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throw(StopException())
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end
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"""
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train!(model, loss, data, opt)
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For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
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backpropagation and calls the optimizer `opt`.
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Takes a callback as keyword argument `cb`. For example, this will print "training"
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every 10 seconds:
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```julia
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Flux.train!(model, loss, data, opt,
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cb = throttle(() -> println("training"), 10))
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```
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The callback can return `:stop` to interrupt the training loop.
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Multiple optimisers and callbacks can be passed to `opt` and `cb` as arrays.
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"""
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function train!(loss, ps, data, opt; cb = () -> ())
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cb = runall(cb)
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opt = runall(opt)
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@progress for d in data
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try
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l = loss(d...)
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@interrupts back!(l)
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update!(opt, ps)
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if cb() == :stop
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depwarn("Use of `:stop` is deprecated; use `Flux.stop()` instead", :stop)
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break
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end
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catch ex
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if ex isa StopException
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break
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else
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rethrow(ex)
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end
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end
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end
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end
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"""
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@epochs N body
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Run `body` `N` times. Mainly useful for quickly doing multiple epochs of
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training in a REPL.
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```julia
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julia> @epochs 2 println("hello")
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INFO: Epoch 1
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hello
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INFO: Epoch 2
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hello
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```
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"""
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macro epochs(n, ex)
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:(@progress for i = 1:$(esc(n))
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@info "Epoch $i"
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$(esc(ex))
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end)
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end
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