rm chainseq

docs/src/models/recurrence.md

@@ -103,6 +103,13 @@ m(seq) # returns a new Seq of length 10
 
 When we apply the model `m` to a seq, it gets mapped over every item in the sequence in order. This is just like the code above, but often more convenient.
 
+You can get this behaviour more generally with the `Over` wrapper.
+
+```julia
+m = Over(Dense(10,5))
+m(seq) # returns a new Seq of length 10
+```
+
 ## Truncating Gradients
 
 By default, calculating the gradients in a recurrent layer involves the entire history. For example, if we call the model on 100 inputs, calling `back!` will calculate the gradient for those 100 calls. If we then calculate another 10 inputs we have to calculate 110 gradients – this accumulates and quickly becomes expensive.

src/Flux.jl

@@ -7,7 +7,7 @@ module Flux
 using Juno
 using Lazy: @forward
 
-export Chain, Dense, Seq, ChainSeq, RNN, LSTM,
+export Chain, Dense, Seq, Over, RNN, LSTM,
   SGD, params
 
 using NNlib

src/layers/recurrent.jl

@@ -13,26 +13,14 @@ Seq(xs) = Seq(collect(xs))
 
 Base.getindex(s::Seq, i) = s.data[i]
 
-type ChainSeq
-  layers::Vector{Any}
-  ChainSeq(xs...) = new([xs...])
+struct Over{T}
+  m::T
 end
 
-@forward ChainSeq.layers Base.getindex, Base.first, Base.last, Base.endof, Base.push!
-@forward ChainSeq.layers Base.start, Base.next, Base.done
+(m::Over)(xs...) = m.m(xs...)
+(m::Over)(xs::Seq) = Seq(map(m, xs.data))
 
-Optimise.children(c::ChainSeq) = c.layers
-
-(c::ChainSeq)(x) = foldl((x, m) -> m(x), x, c.layers)
-(c::ChainSeq)(s::Seq) = Seq([c(x) for x in s.data])
-
-Base.getindex(c::ChainSeq, i::AbstractArray) = Chain(c.layers[i]...)
-
-function Base.show(io::IO, c::ChainSeq)
-  print(io, "ChainSeq(")
-  join(io, c.layers, ", ")
-  print(io, ")")
-end
+Base.show(io::IO, m::Over) = print(io, "Over(", m.m, ")")
 
 # Stateful recurrence
 
@@ -49,7 +37,7 @@ function (m::Recur)(xs...)
   return y
 end
 
-(m::Recur)(s::Seq) = Seq([m(x) for x in s.data])
+(m::Recur)(s::Seq) = Seq(map(m, x.data))
 
 Optimise.children(m::Recur) = (m.cell,)