func costFuncRDeriv(c neuralnet.CostFunc, expected, actual,
actualR linalg.Vector) (deriv, rDeriv linalg.Vector) {
variable := &autofunc.RVariable{
Variable: &autofunc.Variable{Vector: actual},
ROutputVec: actualR,
}
deriv = make(linalg.Vector, len(actual))
rDeriv = make(linalg.Vector, len(actual))
res := c.CostR(autofunc.RVector{}, expected, variable)
res.PropagateRGradient([]float64{1}, []float64{0},
autofunc.RGradient{variable.Variable: rDeriv},
autofunc.Gradient{variable.Variable: deriv})
return
}
