//TODO the activation function and his Derviate has to be more general.. to implemente soft-max for example
func (this *ANN) ForwardPropagation(In *Matrix.Matrix) (As, AsDerviate *([]*Matrix.Matrix), Output *Matrix.Matrix) {
if In.GetMRows() == this.Inputs && In.GetNColumns() == 1 {
As1 := make([]*Matrix.Matrix, len(this.Weights)+1, len(this.Weights)+1)
AsDerviate1 := make([]*Matrix.Matrix, len(this.Weights)+1, len(this.Weights)+1)
As := &As1
AsDerviate = &AsDerviate1
sTemp := In.Transpose()
//Add a new column for a Bias Weight
sTemp = sTemp.AddColumn(Matrix.I(1))
holeInput := sTemp.Copy()
As1[0] = sTemp.Transpose()
//Derivate
//sutract, _ := Matrix.Sustract(Matrix.OnesMatrix(As1[0].GetMRows(), 1), As1[0])
//derivate := Matrix.DotMultiplication(As1[0], sutract)
//derivate := holeInput.Apply(this.Derivate)
derivate := this.DarivateActivationLayer(holeInput)
AsDerviate1[0] = derivate.Transpose()
for i := 0; i < len(this.Weights); i++ {
sTemp = Matrix.Product(sTemp, (this.Weights[i]))
//apply the activation functions
holeInput := sTemp.Copy()
sTemp = this.ActivationLayer(sTemp)
//sTemp = sTemp.Apply(this.Activation)
//Add a new column for a Bias Weight
sTemp = sTemp.AddColumn(Matrix.I(1))
(*As)[i+1] = sTemp.Transpose()
//Derivate
//sutract, _ := Matrix.Sustract(Matrix.OnesMatrix((*As)[i+1].GetMRows(), 1), (*As)[i+1])
//derivate := Matrix.DotMultiplication((*As)[i+1], sutract)
derivate := this.DarivateActivationLayer(holeInput)
//derivate := holeInput.Apply(this.Derivate)
(*AsDerviate)[i+1] = derivate.Transpose()
}
Asf := sTemp.Copy()
//Asf = Asf.AddColumn(Matrix.I(1))
(*As)[len(As1)-1] = Asf.Transpose()
Output = sTemp.Transpose().MatrixWithoutLastRow()
return As, AsDerviate, Output
}
return nil, nil, nil
}
func (this *ANN) BackPropagation(As, AsDerviate *[](*Matrix.Matrix), ForwardOutput *Matrix.Matrix, Y *Matrix.Matrix, flen float64) {
ð := this.DerviateCostFunction(ForwardOutput, Y)
this.ð[len(this.ð)-1] = ð
this.AcumatedError, _ = Matrix.Sum(this.CostFunction(ForwardOutput, Y), this.AcumatedError)
for i := len(this.Weights) - 1; i >= 0; i-- {
A := (*As)[i]
Aderviate := (*AsDerviate)[i]
var ðtemp *Matrix.Matrix
if i == len(this.Weights)-1 {
ðtemp = this.ð[i+1].Transpose()
} else {
ðtemp = this.ð[i+1].MatrixWithoutLastRow().Transpose()
}
//Calc ð
//fmt.Println("ð(i+1)", this.ð[i+1].ToString())
//fmt.Println("W(i)", this.Weights[i].ToString())
Product := Matrix.Product(this.Weights[i], ðtemp.Transpose())
//fmt.Println("Product", i, " ", Product.ToString())
this.ð[i] = Matrix.DotMultiplication(Product, Aderviate.AddRowsToDown(Matrix.I(1)))
//Calc of Derivate with respect to the Weights
//ðtemp:= i==len(this.Weights) - 1? this.ð[i+1].Transpose() : this.ð[i+1].MatrixWithoutLastRow().Transpose()
Dw := Matrix.Product(A, ðtemp)
this.Δ[i], _ = Matrix.Sum(this.Δ[i], Dw)
}
return
}