tweaks

src/cuda/cudnn.jl

@@ -83,7 +83,7 @@ function RNNDesc{T}(mode::Int, input::Int, hidden::Int; layers = 1) where T
   @check ccall((:cudnnSetRNNDescriptor_v6,libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Cint,Ptr{Nothing},Cint,Cint,Cint,Cint,Cint),
     libcudnn_handle[],d[],hidden,layers,dropoutDesc,inputMode,direction,mode,algo,cudnnDataType(T))
 
-  w = cuzero(T, rnnParamSize(T, d[], input))
+  w = cuzeros(T, rnnParamSize(T, d[], input))
   # TODO: avoid reserve allocation here
   rd = RNNDesc{T}(mode, input, hidden, w, params(w, input, hidden, ngates(mode))..., d[])
   finalizer(rd, x ->

src/layers/normalise.jl

@@ -108,9 +108,9 @@ mutable struct BatchNorm{F,V,W,N}
 end
 
 BatchNorm(chs::Integer, λ = identity;
-          initβ = (i) -> fill(0.0,i), initγ = (i) -> fill(1.0,i), ϵ = 1e-8, momentum = .1) =
+          initβ = (i) -> zeros(i), initγ = (i) -> ones(i), ϵ = 1e-8, momentum = .1) =
   BatchNorm(λ, param(initβ(chs)), param(initγ(chs)),
-            fill(0.0,chs), fill(1.0,chs), ϵ, momentum, true)
+            zeros(chs), ones(chs), ϵ, momentum, true)
 
 function (BN::BatchNorm)(x)
   size(x, ndims(x)-1) == length(BN.β) ||

src/layers/recurrent.jl

@@ -84,7 +84,7 @@ end
 RNNCell(in::Integer, out::Integer, σ = tanh;
         init = glorot_uniform) =
   RNNCell(σ, param(init(out, in)), param(init(out, out)),
-          param(fill(0.0,out)), param(initn(out)))
+          param(zeros(out)), param(initn(out)))
 
 function (m::RNNCell)(h, x)
   σ, Wi, Wh, b = m.σ, m.Wi, m.Wh, m.b