Reverse Mode Automatic Differentiation with Continuation Passing Style

This project implements reverse mode automatic differentiation with continuation passing style (CPS). Tensors are supported with number type of float32, float64, complex64, or complex128. Gradient was inspired by Lantern as described by this paper.

Feedfoward example

data := [...][3]float64{
	{0, 0, 0},
	{1, 0, 1},
	{0, 1, 1},
	{1, 1, 0},
}
context := Context[float64]{}
set := context.NewSet()
set.Add("w0", 2, 8)
set.AddBias("b0", 8)
set.Add("w1", 16, 1)
set.AddBias("b1", 1)
set.AddData("input", 2, 4)
set.AddData("output", 1, 4)

rng := rand.New(rand.NewSource(2))
set.InitAdam(rng)
input := set.ByName["input"]
output := set.ByName["output"]
index0, index1 := 0, 0
for _, d := range data {
	input.X[index0] = d[0]
	index0++
	input.X[index0] = d[1]
	index0++
	output.X[index1] = d[2]
	index1++
}

Add := context.B(context.Add)
Mul := context.B(context.Mul)
Everett := context.U(context.Everett)
Sigmoid := context.U(context.Sigmoid)
Quadratic := context.B(context.Quadratic)
Avg := context.U(context.Avg)
l0 := Everett(Add(Mul(set.Get("w0"), set.Get("input")), set.Get("b0")))
l1 := Sigmoid(Add(Mul(set.Get("w1"), l0), set.Get("b1")))
loss := Avg(Quadratic(l1, set.Get("output")))
for range 33 {
	set.Zero()
	l := Gradient(loss)
	set.Adam(B1, B2, .1)
	t.Log(l)
}

l1(func(a *V[float64]) bool {
	x := a.X
	t.Log(x)
	for i, d := range data {
		if d[2] == 0 && x[i] > .5 {
			t.Fatal("incorrect", 0, x[i])
		} else if d[2] == 1 && x[i] <= .5 {
			t.Fatal("incorrect", 1, x[i])
		}
	}
	return true
})