split out core

src/Flux.jl

@@ -20,9 +20,12 @@ export @net, unroll, unroll1, @shapes,
 include("Batches/Batches.jl")
 using .Batches
 
+include("core.jl")
+import .FluxCore: back!, update!, graph
+
 include("utils.jl")
 
-include("model.jl")
+include("params.jl")
 
 include("compiler/code.jl")
 include("compiler/loops.jl")

src/compiler/interp.jl

@@ -25,3 +25,13 @@ function interpmodel(m, args...)
   ctx = Context(mux(iconst, iline, ilambda, iargs, ituple, interp))
   @ithrow interp(ctx, m, args...)
 end
+
+# Anonymous models
+
+struct Capacitor
+  graph::IVertex{Any}
+end
+
+(m::Capacitor)(xs...) = interpmodel(m, xs...)
+
+graph(cap::Capacitor) = cap.graph

src/core.jl

@@ -1,3 +1,8 @@
+# This code is in a submodule with the intention that it will be split into an
+# interface package.
+
+module FluxCore
+
 """
     back!(model, ΔY, X...) => ΔX
 
@@ -27,57 +32,4 @@ methods as necessary.
 """
 graph(m) = nothing
 
-# Model parameters
-
-# TODO: should be AbstractArray?
-"""
-A `Param` object stores a parameter array along with its gradient.
-When converting to backends like TensorFlow, identical `Param`s will
-result in identical variable objects.
-"""
-struct Param{T}
-  x::T
-  Δx::T
 end
-
-"""
-    param(x::T) => ::Param{T}
-
-Convenience method for creating a `Param` object for a given array.
-"""
-param(x) = Param(x, zero(x))
-
-state(p::Param) = p.x
-
-"""
-    update!(p::Param)
-
-Apply the accumulated updates to the value of the parameter.
-"""
-function update!(p::Param, η)
-  p.x .-= p.Δx .* η
-  p.Δx[:] = 0
-  return p
-end
-
-state(x) = x
-
-Base.size(p::Param) = size(p.x)
-Base.size(p::Param, n) = size(p.x, n)
-
-function Base.show(io::IO, p::Param)
-  print(io, "Param", size(p.x))
-end
-
-Base.copy!(xs, p::Param) = copy!(xs, p.x)
-Base.copy!(p::Param, xs) = copy!(p.x, xs)
-
-# Anonymous models
-
-struct Capacitor
-  graph::IVertex{Any}
-end
-
-(m::Capacitor)(xs...) = interpmodel(m, xs...)
-
-graph(cap::Capacitor) = cap.graph

src/params.jl

@@ -0,0 +1,41 @@
+"""
+A `Param` object stores a parameter array along with its gradient.
+When converting to backends like TensorFlow, identical `Param`s will
+result in identical variable objects.
+"""
+struct Param{T}
+  x::T
+  Δx::T
+end
+
+"""
+    param(x::T) => ::Param{T}
+
+Convenience method for creating a `Param` object for a given array.
+"""
+param(x) = Param(x, zero(x))
+
+state(p::Param) = p.x
+
+"""
+    update!(p::Param)
+
+Apply the accumulated updates to the value of the parameter.
+"""
+function update!(p::Param, η)
+  p.x .-= p.Δx .* η
+  p.Δx[:] = 0
+  return p
+end
+
+state(x) = x
+
+Base.size(p::Param) = size(p.x)
+Base.size(p::Param, n) = size(p.x, n)
+
+function Base.show(io::IO, p::Param)
+  print(io, "Param", size(p.x))
+end
+
+Base.copy!(xs, p::Param) = copy!(xs, p.x)
+Base.copy!(p::Param, xs) = copy!(p.x, xs)