format

Author: zhujch1

ext/TaylorDiffNNlibExt.jl

@@ -4,14 +4,15 @@ using TaylorDiff
 import NNlib: oftf
 import NNlib: sigmoid_fast, tanh_fast, rrelu, leakyrelu
 
-
 @inline sigmoid_fast(t::TaylorScalar) = one(t) / (one(t) + exp(-t))
 
 @inline tanh_fast(t::TaylorScalar) = tanh(t)
 
-@inline function rrelu(t::TaylorScalar{T, N}, l=oftf(t, 1/8), u=oftf(t, 1/3)) where {T, N}
+@inline function rrelu(t::TaylorScalar{T, N},
+        l = oftf(t, 1 / 8),
+        u = oftf(t, 1 / 3)) where {T, N}
     a = (u - l) * rand(float(T)) + l
     return leakyrelu(t, a)
 end
 
-end
+end

src/codegen.jl

@@ -11,7 +11,7 @@ for func in (+, -, deg2rad, rad2deg,
     asin, acos, atan, asec, acsc, acot,
     log, log10, log1p, log2,
     asinh, acosh, atanh, asech, acsch,
-    acoth, 
+    acoth,
     abs, sign)
     F = typeof(func)
     # base case

src/scalar.jl

@@ -89,4 +89,6 @@ for op in (:+, :-, :*, :/)
 end
 transpose(t::TaylorScalar) = t
 
-Base.AbstractFloat(x::TaylorScalar{T, N}) where {T, N} = TaylorScalar{Float64, N}(convert(NTuple{N, Float64}, x.value))
+function Base.AbstractFloat(x::TaylorScalar{T, N}) where {T, N}
+    TaylorScalar{Float64, N}(convert(NTuple{N, Float64}, x.value))
+end