NNlibExt

Project.toml

@@ -12,16 +12,19 @@ Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [weakdeps]
+NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 
 [extensions]
+TaylorDiffNNlibExt = ["NNlib"]
 TaylorDiffSFExt = ["SpecialFunctions"]
 
 [compat]
 ChainRules = "1"
 ChainRulesCore = "1"
 ChainRulesOverloadGeneration = "0.1"
 SpecialFunctions = "2"
+NNlib = "0.9"
 SymbolicUtils = "1"
 Symbolics = "5"
 Zygote = "0.6.55"

ext/TaylorDiffNNlibExt.jl

@@ -0,0 +1,18 @@
+module TaylorDiffNNlibExt
+
+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}
+    a = (u - l) * rand(float(T)) + l
+    return leakyrelu(t, a)
+end
+
+end

ext/TaylorDiffSFExt.jl

@@ -8,7 +8,8 @@ using ChainRules, ChainRulesCore
 
 dummy = (NoTangent(), 1)
 @variables z
-for func in (erf,)
+# logerfc, logerfcx, erfinv, gamma, digamma, trigamma
+for func in (erf, erfc, erfcinv, erfcx, erfi)
     F = typeof(func)
     # base case
     @eval function (op::$F)(t::TaylorScalar{T, 2}) where {T}

src/codegen.jl

@@ -11,7 +11,8 @@ 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
     @eval function (op::$F)(t::TaylorScalar{T, 2}) where {T}

src/derivative.jl

@@ -38,7 +38,7 @@ end
 # Core APIs
 
 # Added to help Zygote infer types
-make_taylor(t0::T, t1::S, ::Val{N}) where {T, S, N} = TaylorScalar{T, N}(t0, T(t1))
+make_taylor(t0::T, t1::S, ::Val{N}) where {T, S, N} = TaylorScalar{T, N}(t0, convert(T, t1))
 
 @inline function derivative(f, x::T, ::Val{N}) where {T <: TN, N}
     t = TaylorScalar{T, N}(x, one(x))

src/scalar.jl

@@ -88,3 +88,7 @@
     @eval @inline $op(a::Number, b::TaylorScalar) = $op(promote(a, b)...)
 end
 transpose(t::TaylorScalar) = t
+
+function Base.AbstractFloat(x::TaylorScalar{T, N}) where {T, N}
+    TaylorScalar{Float64, N}(convert(NTuple{N, Float64}, x.value))
+end