model abstract is pretty useless

src/backend/mxnet/model.jl

@@ -81,7 +81,7 @@ end
 
 # TODO: if `last` changes, update params appropriately
 
-mutable struct Model <: Flux.Model
+mutable struct Model
   model::Any
   execs::Dict{Tuple,Exec}
   graph::Graph

src/backend/tensorflow/graph.jl

@@ -1,4 +1,5 @@
 using Base: @get!
+using Flux: Reshape, MaxPool, flatten
 using DataFlow: constant, Split
 using DataFlow.Interpreter
 using DataFlow.Interpreter: stack
@@ -86,8 +87,9 @@ function tograph(model, args...; variables = false)
   return ctx[:params], ctx[:stacks], out
 end
 
-TensorFlow.Tensor(m::Flux.Model, args...) =
+# TODO: replace this
-  tograph(m, args...; variables = true)[3]
+# TensorFlow.Tensor(m::Flux.Model, args...) =
+#   tograph(m, args...; variables = true)[3]
 
 RawTensor(data::Union{Batch,Seq}) = RawTensor(rawbatch(data))
 

src/compiler/code.jl

@@ -28,7 +28,7 @@ end
 function build_type(T, params)
   @esc T
   ex = quote
-    type $T <: Model
+    type $T
       $(params...)
     end
   end

src/compiler/loops.jl

@@ -2,7 +2,7 @@ export unroll, unroll1
 
 # Stateful Models
 
-mutable struct Stateful <: Model
+mutable struct Stateful
   model
   states::Vector{Any}
   istate::Vector{Any}

src/compiler/shape.jl

@@ -67,7 +67,7 @@ end
 
 export Input
 
-struct Input{N} <: Model
+struct Input{N}
   dims::Dims{N}
 end
 

src/dims/seq.jl

@@ -29,7 +29,7 @@ end
 
 # SeqModel wrapper layer for convenience
 
-struct SeqModel <: Model
+struct SeqModel
   model
   steps::Int
 end

src/layers/control.jl

@@ -1,6 +1,6 @@
 export Chain, @Chain
 
-type Chain <: Model
+type Chain
   layers::Vector{Any}
   Chain(xs...) = new([xs...])
 end

src/layers/shims.jl

@@ -1,6 +1,6 @@
 export Conv2D
 
-struct Conv2D <: Model
+struct Conv2D
   filter::Param{Array{Float64,4}} # [height, width, inchans, outchans]
   stride::Dims{2}
 end
@@ -15,7 +15,7 @@ infer(c::Conv2D, in::Dims{4}) =
 
 for Pool in :[MaxPool, AvgPool].args
   @eval begin
-    struct $Pool <: Model
+    struct $Pool
       size::Dims{2}
       stride::Dims{2}
     end

src/model.jl

@@ -1,31 +1,14 @@
-# Basic model API
-
 """
-    (m::Model)(X...) => Y
+    back!(model, ΔY, X...) => ΔX
-
-A "model" is a function with state. For example, a logistic regression is the
-function
-
-    x -> σ(x * W + b)
-
-where `W` and `b` are a trainable matrix and vector of weights repectively. The
-`Model` abstract type is used loosely; in general the concept of a model is
-closer to a protocol, and models don't need to inherit from this type. Normal
-Julia functions are models with 0 parameters, for example.
-"""
-abstract type Model end
-
-"""
-    back!(m::Model, ΔY, X...) => ΔX
 
 Backpropagate the gradient `ΔY` through the model `m`, accumulating the
 gradients of any parameters. Returns the gradient of the input `X`. Gradients
 may be arrays or tuples of arrays (for multiple inputs/outputs).
 """
-back!(m::Model, Δ, xs...) = error("Backprop not implemented for $(typeof(m))")
+back!(model, Δ, xs...) = error("Backprop not implemented for $(typeof(m))")
 
 """
-    update!(m::Model, η) => m
+    update!(model, η) => m
 
 Update the parameters of the model `m` using the accumulated gradients from
 `back!`, using the learning rate `η`.
@@ -33,7 +16,7 @@ Update the parameters of the model `m` using the accumulated gradients from
 update!(m, η) = m
 
 """
-    graph(m::Model) => ::IVertex{Any} | nothing
+    graph(model) => ::IVertex{Any} | nothing
 
 Returns the graph representation of the model, if any. Most models are built
 from lower-level components and can simply implement this method to get most of
@@ -91,7 +74,7 @@ Base.copy!(p::Param, xs) = copy!(p.x, xs)
 
 # Anonymous models
 
-struct Capacitor <: Model
+struct Capacitor
   graph::IVertex{Any}
 end