some more changes

src/tracker/array.jl

@@ -221,7 +221,7 @@ Base.prod(xs::TrackedArray) = track(prod, xs)
 Base.prod(f::Union{Function, Type}, xs::TrackedArray) = prod(f.(xs))
 
 @grad prod(xs) = prod(data(xs)), Δ -> (prod(xs) ./ xs .* Δ,)
-@grad prod(xs, dim) = prod(data(xs), dim),
+@grad prod(xs, dim) = prod(data(xs), dims = dim),
   Δ -> (nobacksies(:sum,
           reshape(.*(circshift.([reshape(data(xs), length(xs))], 1:length(xs)-1)...), size(xs)) .* Δ),
         nothing)

src/tracker/numeric.jl

@@ -1,5 +1,5 @@
 function ngradient(f, xs::AbstractArray...)
-  grads = zeros.(xs)
+  grads = zero.(xs)
   for (x, Δ) in zip(xs, grads), i in 1:length(x)
     δ = sqrt(eps())
     tmp = x[i]

test/tracker.jl

@@ -2,7 +2,7 @@ using Flux
 using Flux.Tracker, Base.Test, NNlib
 using Flux.Tracker: TrackedReal, gradcheck, grad, derivative, checkpoint
 using NNlib: conv
-using StatsBase
+# using StatsBase
 
 gradtest(f, xs::AbstractArray...) = gradcheck((xs...) -> sum(sin.(f(xs...))), xs...)
 gradtest(f, dims...) = gradtest(f, rand.(dims)...)
@@ -14,13 +14,13 @@ gradtest(f, dims...) = gradtest(f, rand.(dims)...)
 @test gradtest((x, W, b) -> logσ.(W*x .+ b), 5, (2,5), 2)
 @test gradtest((x, W, b) -> logσ.(W*x .+ b), (5,3), (2,5), 2)
 
-@test gradtest((w, x) -> w'*x, randn(10, 2), randn(10))
-@test gradtest((w, x) -> w*x', randn(5,5), randn(5,5))
+@test gradtest((w, x) -> w'*x, randn(Float64,10, 2), randn(Float64,10))
+@test gradtest((w, x) -> w*x', randn(Float64,5,5), randn(Float64,5,5))
 
 @test gradtest(x -> sum(x, dims = (2, 3)), (3,4,5))
-@test gradtest(x -> sum(x, dims = 1), randn(2,3))
-@test gradtest(x -> sum(x, dims = [1,2]), randn(2,3))
-@test gradtest(x -> sum(x), randn(2,3))
+@test gradtest(x -> sum(x, dims = 1), randn(Float64,2,3))
+@test gradtest(x -> sum(x, dims = [1,2]), randn(Float64,2,3))
+@test gradtest(x -> sum(x), randn(Float64,2,3))
 @test gradtest(x -> prod(x, (2, 3)), (3,4,5))
 @test gradtest(x -> prod(x), (3,4,5))
 
@@ -170,9 +170,9 @@ end
   2y + x
 end
 
-@test gradtest(conv, rand(10, 3, 2), randn(2, 3, 2))
-@test gradtest(conv, rand(10, 10, 3, 2), randn(2, 2, 3, 2))
-@test gradtest(conv, rand(10, 10, 10, 3, 2), randn(2, 2, 2, 3, 2))
+@test gradtest(conv, rand(10, 3, 2), randn(Float64,2, 3, 2))
+@test gradtest(conv, rand(10, 10, 3, 2), randn(Float64,2, 2, 3, 2))
+@test gradtest(conv, rand(10, 10, 10, 3, 2), randn(Float64,2, 2, 2, 3, 2))
 
 @test gradtest(x -> maxpool(x, (2,2)), rand(10, 10, 3, 2))
 @test gradtest(x -> maxpool(x, (2,2,2)), rand(10, 10, 10, 3, 2))
@@ -216,7 +216,7 @@ end
 
 @test @sprintf("%.2f", sum(param([1,2,3]))) == "6.00"
 
-@inferred NNlib.conv(param(rand(10,10,3,2)),randn(2,2,3,4))
+@inferred NNlib.conv(param(rand(10,10,3,2)),randn(Float64,2,2,3,4))
 
 b = param(rand())
 Tracker.back!(b)