A cleaner more powerful train! function

Project.toml

@@ -6,12 +6,14 @@ version = "0.1.0"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 Flux = "0.13.7"
 NNlib = "0.8.10"
 Optimisers = "0.2.10"
+ProgressMeter = "1.7.2"
 Zygote = "0.6.49"
 julia = "1.6"
 

README.md

@@ -34,4 +34,4 @@ As will any features which migrate to Flux itself.
 ## Current Features
 
 * Layers `Split` and `Join`
-
+* A more advanced `train!`

src/Fluxperimental.jl

@@ -5,4 +5,7 @@ using Flux
 include("split_join.jl")
 export Split, Join
 
+include("train.jl")
+export shinkansen!
+
 end # module Fluxperimental

src/train.jl

@@ -0,0 +1,78 @@
+using Flux: withgradient, DataLoader
+using Optimisers: Optimisers
+using ProgressMeter: ProgressMeter, Progress, next!
+
+#=
+
+This grew out of explicit-mode upgrade here:
+https://github.com/FluxML/Flux.jl/pull/2082
+
+=#
+
+"""
+    shinkansen!(loss, model, data...; state, epochs=1, [batchsize, keywords...])
+
+This is a re-design of `train!`:
+
+* The loss function must accept the remaining arguments: `loss(model, data...)`
+* The optimiser state from `setup` must be passed to the keyword `state`.
+
+By default it calls `gradient(loss, model, data...)` just like that.
+Same order as the arguments. If you specify `epochs = 100`, then it will do this 100 times.
+
+But if you specify `batchsize = 32`, then it first makes `DataLoader(data...; batchsize)`,
+and uses that to generate smaller arrays to feed to `gradient`.
+All other keywords are passed to `DataLoader`, e.g. to shuffle batches.
+
+Returns the loss from every call.
+
+# Example
+```
+X = repeat(hcat(digits.(0:3, base=2, pad=2)...), 1, 32)
+Y = Flux.onehotbatch(xor.(eachrow(X)...), 0:1)
+
+model = Chain(Dense(2 => 3, sigmoid), BatchNorm(3), Dense(3 => 2))
+state = Flux.setup(Adam(0.1, (0.7, 0.95)), model)
+# state = Optimisers.setup(Optimisers.Adam(0.1, (0.7, 0.95)), model)  # for now
+
+shinkansen!(model, X, Y; state, epochs=100, batchsize=16, shuffle=true) do m, x, y
+    Flux.logitcrossentropy(m(x), y)
+end
+
+all((softmax(model(X)) .> 0.5) .== Y)
+```
+"""
+function shinkansen!(loss::Function, model, data...; state, epochs=1, batchsize=nothing, kw...)
+  if batchsize != nothing
+    loader = DataLoader(data; batchsize, kw...)
+    losses = Vector{Float32}[]
+    prog = Progress(length(loader) * epochs)
+
+    for e in 1:epochs
+      eplosses = Float32[]
+      for (i,d) in enumerate(loader)
+        l, (g, _...) = withgradient(loss, model, d...)
+        isfinite(l) || error("loss is $l, on batch $i, epoch $epoch")
+        Optimisers.update!(state, model, g)
+        push!(eplosses, l)
+        next!(prog; showvalues=[(:epoch, e), (:loss, l)])
+      end
+      push!(losses, eplosses)
+    end
+
+    return allequal(size.(losses)) ? reduce(hcat, losses) : losses
+  else
+    losses = Float32[]
+    prog = Progress(epochs)
+
+    for e in 1:epochs
+      l, (g, _...) = withgradient(loss, model, data...)
+      isfinite(l) || error("loss is $l, on epoch $epoch")
+      Optimisers.update!(state, model, g)
+      push!(losses, l)
+      next!(prog; showvalues=[(:epoch, epoch), (:loss, l)])
+    end
+
+    return losses
+  end
+end

test/train.jl

@@ -0,0 +1,27 @@
+import Flux, Fluxperimental, Optimisers
+
+@testset "shinkansen!" begin
+
+  X = repeat(hcat(digits.(0:3, base=2, pad=2)...), 1, 32)
+  Y = Flux.onehotbatch(xor.(eachrow(X)...), 0:1)
+
+  model = Flux.Chain(Flux.Dense(2 => 3, Flux.sigmoid), Flux.BatchNorm(3), Flux.Dense(3 => 2))
+  state = Optimisers.setup(Optimisers.Adam(0.1, (0.7, 0.95)), model)
+
+  Fluxperimental.shinkansen!(model, X, Y; state, epochs=100) do m, x, y
+      Flux.logitcrossentropy(m(x), y)
+  end
+
+  @test all((Flux.softmax(model(X)) .> 0.5) .== Y)
+
+  model = Flux.Chain(Flux.Dense(2 => 3, Flux.sigmoid), Flux.BatchNorm(3), Flux.Dense(3 => 2))
+  state = Optimisers.setup(Optimisers.Adam(0.1, (0.7, 0.95)), model)
+
+  Fluxperimental.shinkansen!(model, X, Y; state, epochs=100, batchsize=16, shuffle=true) do m, x, y
+      Flux.logitcrossentropy(m(x), y)
+  end
+
+  @test all((Flux.softmax(model(X)) .> 0.5) .== Y)
+
+end
+