diff --git a/src/NeuralNets.jl b/src/NeuralNets.jl
index 9fb0060..8b6d9d3 100644
--- a/src/NeuralNets.jl
+++ b/src/NeuralNets.jl
@@ -4,7 +4,7 @@ using Optim
 using ArrayViews
 
 import Optim:levenberg_marquardt
-import Base: show
+importall Base
 
 
 # functions
diff --git a/src/activations.jl b/src/activations.jl
index b14c469..2b5724f 100644
--- a/src/activations.jl
+++ b/src/activations.jl
@@ -14,13 +14,13 @@ identd(x) = 1
 tanhd(x) = sech(x).^2
 
 # dictionary of commonly-used activation derivatives
-derivs = Dict{Function, Function}([
+derivs = Dict{Function, Function}((
                                    logis     => logisd, 
                                    logissafe => logissafed,
                                    relu      => relud, 
                                    ident     => identd, 
                                    tanh      => tanhd
-                                   ])
+                                   ))
 
 # automatic differentiateion with ForwardDiff.jl
 # due to limitations of ForwardDiff.jl, this function
@@ -29,4 +29,4 @@ function autodiff(activ::Function)
     f(x) = activ(x[1])
     forwarddiff_derivative(x::Float64) = forwarddiff_gradient(f,Float64)([x])[1]
     return forwarddiff_derivative
-end
\ No newline at end of file
+end