rm Over Seq

docs/src/models/recurrence.md

@@ -91,23 +91,14 @@ seq = [rand(10) for i = 1:10]
 With `Recur`, applying our model to each element of a sequence is trivial:
 
 ```julia
-map(m, seq) # returns a list of 5-element vectors
+m.(seq) # returns a list of 5-element vectors
 ```
 
-To make this a bit more convenient, Flux has the `Seq` type. This is just a list, but tagged so that we know it's meant to be used as a sequence of data points.
+This works even when we've chain recurrent layers into a larger model.
 
 ```julia
-seq = Seq([rand(10) for i = 1:10])
+m = Chain(LSTM(10, 15), Dense(15, 5))
-m(seq) # returns a new Seq of length 10
+m.(seq)
-```
-
-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

src/Flux.jl

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

src/layers/recurrent.jl

@@ -1,29 +1,6 @@
 # TODO: broadcasting cat
 combine(x, h) = vcat(x, h .* trues(1, size(x, 2)))
 
-# Sequences
-
-struct Seq{T,A<:AbstractVector{T}}
-  data::A
-end
-
-Seq(xs::AbstractVector{T}) where T = Seq{T,typeof(xs)}(xs)
-
-Seq(xs) = Seq(collect(xs))
-
-Base.getindex(s::Seq, i) = s.data[i]
-
-struct Over{T}
-  m::T
-end
-
-(m::Over)(xs...) = m.m(xs...)
-(m::Over)(s::Seq) = Seq(map(m, s.data))
-
-Base.show(io::IO, m::Over) = print(io, "Over(", m.m, ")")
-
-Optimise.children(m::Over) = (m.m,)
-
 # Stateful recurrence
 
 mutable struct Recur{T}