use broadcasting plus

src/backend/mxnet/graph.jl

@@ -18,7 +18,7 @@ node(x::Tuple) = map(node, x)
 node(x::mx.SymbolicNode) = x
 
 graph(::typeof(tuple), args...) = (args...,)
-graph(::typeof(+), args...) = mx.broadcast_plus(args...)
+graph(::typeof(.+), args...) = mx.broadcast_plus(args...)
 graph(::typeof(*), xs...) = mx.dot(reverse(xs)...) # Work around MXNet shape hack
 graph(::typeof(σ), x) = mx.Activation(x, act_type = :sigmoid)
 graph(::typeof(relu), x) = mx.Activation(x, act_type = :relu)

src/backend/tensorflow/graph.jl

@@ -19,7 +19,7 @@ graph(::typeof(σ), x) = nn.sigmoid(x)
 graph(::typeof(hcat), xs...) = concat(1, xs)
 graph(::typeof(seq), xs, n) = TensorFlow.unpack(xs, num = n, axis = 1)
 
-for op in (tanh, *, .*, +, -)
+for op in (tanh, *, .*, .+, .-)
   @eval graph(::typeof($op), args...) = $op(args...)
 end
 

src/compiler/shape.jl

@@ -52,7 +52,7 @@ function infer(::typeof(*), a::Dims{2}, b::Dims{2})
   (a[1], b[2])
 end
 
-infer(::typeof(+), xs::Dims...) = Base.Broadcast.broadcast_shape(xs...)
+infer(::typeof(.+), xs::Dims...) = Base.Broadcast.broadcast_shape(xs...)
 
 # Shapes macro
 

src/layers/affine.jl

@@ -3,7 +3,7 @@ export Affine
 @net type Affine
   W
   b
-  x -> x*W + b
+  x -> x*W .+ b
 end
 
 Affine(in::Integer, out::Integer; init = initn) =

src/layers/recurrent.jl

@@ -4,7 +4,7 @@ export Recurrent, GatedRecurrent, LSTM
   Wxy; Wyy; by
   y
   function (x)
-    y = tanh( x * Wxy + y{-1} * Wyy + by )
+    y = tanh( x * Wxy .+ y{-1} * Wyy .+ by )
   end
 end
 
@@ -17,10 +17,10 @@ Recurrent(in, out; init = initn) =
   Wxh; Wyh; bh
   y
   function (x)
-    reset  = σ( x * Wxr + y{-1} * Wyr + br )
-    update = σ( x * Wxu + y{-1} * Wyu + bu )
-    y′ = tanh( x * Wxh + (reset .* y{-1}) * Wyh + bh )
-    y = (1 .- update) .* y′ + update .* y{-1}
+    reset  = σ( x * Wxr .+ y{-1} * Wyr .+ br )
+    update = σ( x * Wxu .+ y{-1} * Wyu .+ bu )
+    y′ = tanh( x * Wxh .+ (reset .* y{-1}) * Wyh .+ bh )
+    y = (1 .- update) .* y′ .+ update .* y{-1}
   end
 end
 
@@ -36,12 +36,12 @@ GatedRecurrent(in, out; init = initn) =
   y; state
   function (x)
     # Gates
-    forget = σ( x * Wxf + y{-1} * Wyf + bf )
-    input  = σ( x * Wxi + y{-1} * Wyi + bi )
-    output = σ( x * Wxo + y{-1} * Wyo + bo )
+    forget = σ( x * Wxf .+ y{-1} * Wyf .+ bf )
+    input  = σ( x * Wxi .+ y{-1} * Wyi .+ bi )
+    output = σ( x * Wxo .+ y{-1} * Wyo .+ bo )
     # State update and output
-    state′ = tanh( x * Wxc + y{-1} * Wyc + bc )
-    state  = forget .* state{-1} + input .* state′
+    state′ = tanh( x * Wxc .+ y{-1} * Wyc .+ bc )
+    state  = forget .* state{-1} .+ input .* state′
     y = output .* tanh(state)
   end
 end

test/basic.jl

@@ -28,7 +28,8 @@ d1 = @net x -> x * d.W + d.b
 @test d(xs) == d1(xs)
 
 let
-  @capture(syntax(d), _Frame(_Line(x_[1] * W_ + b_)))
+  # In 0.6 `.+` evaluates to an anon function, so we must match on that.
+  @capture(syntax(d), _Frame(_Line(bplus_(x_[1] * W_, b_))))
   @test isa(x, DataFlow.Input) && isa(W, Param) && isa(b, Param)
 end