Implement Gated Recurrent Unit

src/Flux.jl

@@ -7,7 +7,7 @@ module Flux
 using Juno, Requires
 using Lazy: @forward
 
-export Chain, Dense, RNN, LSTM, Dropout, LayerNorm,
+export Chain, Dense, RNN, LSTM, GRU, Dropout, LayerNorm,
   SGD, ADAM, Momentum, Nesterov,
   param, params, mapleaves
 

src/layers/recurrent.jl

@@ -150,3 +150,51 @@ See [this article](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
 for a good overview of the internals.
 """
 LSTM(a...; ka...) = Recur(LSTMCell(a...; ka...))
+
+
+
+
+# GRU
+
+struct GRUCell{D1,D2,V}
+  update::D1
+  reset::D1
+  candidate::D2
+  h::V
+end
+
+function GRUCell(in, out; init = initn)
+  cell = GRUCell([Dense(in+out, out, σ, init = init) for _ = 1:2]...,
+                  Dense(in+out, out, tanh, init = init),
+                  param(init(out)))
+  return cell
+end
+
+function (m::GRUCell)(h, x)
+  x′   = combine(x, h)
+  z    = m.update(x′)
+  r    = m.reset(x′)
+  h̃    = m.candidate(combine(r.*h, x))
+  h = (1.-z).*h .+ z.*h̃
+  return h, h
+end
+
+hidden(m::GRUCell) = m.h
+
+treelike(GRUCell)
+
+Base.show(io::IO, m::GRUCell) =
+  print(io, "GRUCell(",
+        size(m.update.W, 2) - size(m.update.W, 1), ", ",
+        size(m.update.W, 1), ')')
+
+"""
+    GRU(in::Integer, out::Integer, σ = tanh)
+
+Gated Recurrent Unit layer. Behaves like an RNN but generally
+exhibits a longer memory span over sequences.
+
+See [this article](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
+for a good overview of the internals.
+"""
+GRU(a...; ka...) = Recur(GRUCell(a...; ka...))