Typo

Author: Avik Pal

src/nnpack/libnnpack.jl

@@ -15,7 +15,7 @@ function nnp_relu_output(batch_size, channels, input, output, negative_slope, th
     @check ccall((:nnp_relu_output, libnnpack), nnp_status, (Csize_t, Csize_t, Ptr{Cfloat}, Ptr{Cfloat}, Cfloat, pthreadpool_t), batch_size, channels, input, output, negative_slope, threadpool)
 end
 
-function nnp_relu_output(x::Array{Float32,N}, y::Array{Float32,N}; negative_slope::Float = 0.0, threadpool = shared_threadpool[]) where {N}
+function nnp_relu_output(x::Array{Float32,N}, y::Array{Float32,N}; negative_slope::AbstractFloat = 0.0, threadpool = shared_threadpool[]) where {N}
     # Investigate why the channel and batch dims need to specified like this
     nnp_relu_output(prod(size(x)[N-1:N]), prod(size(x)[1:N-2]), x, y, negative_slope, threadpool)
     y
@@ -25,7 +25,7 @@ function nnp_relu_input_gradient(batch_size, channels, grad_output, input, grad_
     @check ccall((:nnp_relu_input_gradient, libnnpack), nnp_status, (Csize_t, Csize_t, Ptr{Cfloat}, Ptr{Cfloat}, Ptr{Cfloat}, Cfloat, pthreadpool_t), batch_size, channels, grad_output, input, grad_input, negative_slope, threadpool)
 end
 
-function nnp_relu_input_gradient(x::Array{Float32,N}, dy::Array{Float32,N}, dx::Array{Float32,N}; negative_slope::Float = 0.0, threadpool = shared_threadpool[]) where {N}
+function nnp_relu_input_gradient(x::Array{Float32,N}, dy::Array{Float32,N}, dx::Array{Float32,N}; negative_slope::AbstractFloat = 0.0, threadpool = shared_threadpool[]) where {N}
     # Investigate why the channel and batch dims need to specified like this
     nnp_relu_input_gradient(Csize_t(prod(size(x)[N-1:N])), prod(size(x)[1:N-2]), dy, x, dx, negative_slope, threadpool)
     dx