27 lines
622 B
Julia
27 lines
622 B
Julia
using Juno
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using Flux.Tracker: back!
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tocb(f) = f
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tocb(fs::AbstractVector) = () -> foreach(call, fs)
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"""
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train!(loss, data, opt; cb = () -> ())
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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` and the callback `cb`
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(i.e. `opt()` and `cb()`).
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Multiple callbacks can be passed to `cb` as an array.
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"""
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function train!(loss, data, opt; cb = () -> ())
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cb = tocb(cb)
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@progress for d in data
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l = loss(d...)
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isinf(l.data[]) && error("Loss is Inf")
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isnan(l.data[]) && error("Loss is NaN")
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back!(l)
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opt()
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cb()
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end
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end
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