Mein neuronales Netzwerk ist (von Grund auf) darauf trainiert, sich weiter vom Ziel zu entfernen

func additionneuralnetworktest() {
    nn := newrawai(2, 2, 1, 1/math.pow(10, 15))
    fmt.printf("weights ih before: %v\n\nweights ho after: %v\n", nn.weightsih, nn.weightsho)
    //train neural network
    //
    for epoch := 0; epoch < 10000000; epoch++ {
        for i := 0; i <= 10; i++ {
            for j := 0; j <= 10; j++ {
                inputs := make([]float64, 2)
                targets := make([]float64, 1)
                inputs[0] = float64(i)
                inputs[1] = float64(j)
                targets[0] = float64(i) + float64(j)
                nn.train(inputs, targets)
                if epoch%20000 == 0 && i == 5 && j == 5 {
                    fmt.printf("[training] [epoch %d] %f + %f = %f targets[%f]\n", epoch, inputs[0], inputs[1], nn.outputlayer[0], targets[0])
                }

            }

        }
    }
    // test neural network
    a := rand.intn(10) + 1
    b := rand.intn(10) + 1
    inputs := make([]float64, 2)
    inputs[0] = float64(a)
    inputs[1] = float64(b)
    prediction := nn.feedforward(inputs)[0]
    fmt.printf("%d + %d = %f\n", a, b, prediction)
    fmt.printf("weights ih: %v\n\nweights ho: %v\n", nn.weightsih, nn.weightsho)

}

type RawAI struct {
    InputLayer   []float64   `json:"input_layer"`
    HiddenLayer  []float64   `json:"hidden_layer"`
    OutputLayer  []float64   `json:"output_layer"`
    WeightsIH    [][]float64 `json:"weights_ih"`
    WeightsHO    [][]float64 `json:"weights_ho"`
    LearningRate float64     `json:"learning_rate"`
}

func NewRawAI(inputSize, hiddenSize, outputSize int, learningRate float64) *RawAI {
    nn := RawAI{
        InputLayer:   make([]float64, inputSize),
        HiddenLayer:  make([]float64, hiddenSize),
        OutputLayer:  make([]float64, outputSize),
        WeightsIH:    randomMatrix(inputSize, hiddenSize),
        WeightsHO:    randomMatrix(hiddenSize, outputSize),
        LearningRate: learningRate,
    }
    return &nn
}
func (nn *RawAI) FeedForward(inputs []float64) []float64 {
    // Set input layer
    for i := 0; i < len(inputs); i++ {
        nn.InputLayer[i] = inputs[i]
    }

    // Compute hidden layer
    for i := 0; i < len(nn.HiddenLayer); i++ {
        sum := 0.0
        for j := 0; j < len(nn.InputLayer); j++ {
            sum += nn.InputLayer[j] * nn.WeightsIH[j][i]
        }
        nn.HiddenLayer[i] = sum
        if math.IsNaN(sum) {
            panic(fmt.Sprintf("Sum is NaN on Hidden Layer:\nInput Layer: %v\nHidden Layer: %v\nWeights IH: %v\n", nn.InputLayer, nn.HiddenLayer, nn.WeightsIH))
        }

    }

    // Compute output layer
    for k := 0; k < len(nn.OutputLayer); k++ {
        sum := 0.0
        for j := 0; j < len(nn.HiddenLayer); j++ {
            sum += nn.HiddenLayer[j] * nn.WeightsHO[j][k]
        }
        nn.OutputLayer[k] = sum
        if math.IsNaN(sum) {
            panic(fmt.Sprintf("Sum is NaN on Output Layer:\n Model: %v\n", nn))
        }

    }

    return nn.OutputLayer
}
func (nn *RawAI) Train(inputs []float64, targets []float64) {
    nn.FeedForward(inputs)

    // Compute output layer error
    outputErrors := make([]float64, len(targets))
    for k := 0; k < len(targets); k++ {
        outputErrors[k] = targets[k] - nn.OutputLayer[k]
    }

    // Compute hidden layer error
    hiddenErrors := make([]float64, len(nn.HiddenLayer))
    for j := 0; j < len(nn.HiddenLayer); j++ {
        errorSum := 0.0
        for k := 0; k < len(nn.OutputLayer); k++ {
            errorSum += outputErrors[k] * nn.WeightsHO[j][k]
        }
        hiddenErrors[j] = errorSum * sigmoidDerivative(nn.HiddenLayer[j])
        if math.IsInf(math.Abs(hiddenErrors[j]), 1) {
            //Find out why
            fmt.Printf("Hidden Error is Infinite:\nTargets:%v\nOutputLayer:%v\n\n", targets, nn.OutputLayer)
        }
    }

    // Update weights
    for j := 0; j < len(nn.HiddenLayer); j++ {
        for k := 0; k < len(nn.OutputLayer); k++ {
            delta := nn.LearningRate * outputErrors[k] * nn.HiddenLayer[j]
            nn.WeightsHO[j][k] += delta
        }
    }
    for i := 0; i < len(nn.InputLayer); i++ {
        for j := 0; j < len(nn.HiddenLayer); j++ {
            delta := nn.LearningRate * hiddenErrors[j] * nn.InputLayer[i]
            nn.WeightsIH[i][j] += delta
            if math.IsNaN(delta) {
                fmt.Print(fmt.Sprintf("Delta is NaN.\n Learning Rate: %f\nHidden Errors: %f\nInput: %f\n", nn.LearningRate, hiddenErrors[j], nn.InputLayer[i]))
            }
            if math.IsNaN(nn.WeightsIH[i][j]) {
                fmt.Print(fmt.Sprintf("Delta is NaN.\n Learning Rate: %f\nHidden Errors: %f\nInput: %f\n", nn.LearningRate, hiddenErrors[j], nn.InputLayer[i]))
            }
        }
    }

}
func (nn *RawAI) ExportWeights(filename string) error {
    weightsJson, err := json.Marshal(nn)
    if err != nil {
        return err
    }
    err = ioutil.WriteFile(filename, weightsJson, 0644)
    if err != nil {
        return err
    }
    return nil
}
func (nn *RawAI) ImportWeights(filename string) error {
    weightsJson, err := ioutil.ReadFile(filename)
    if err != nil {
        return err
    }
    err = json.Unmarshal(weightsJson, nn)
    if err != nil {
        return err
    }
    return nil
}

//RawAI Tools:
func randomMatrix(rows, cols int) [][]float64 {
    matrix := make([][]float64, rows)
    for i := 0; i < rows; i++ {
        matrix[i] = make([]float64, cols)
        for j := 0; j < cols; j++ {
            matrix[i][j] = 1.0
        }
    }
    return matrix
}
func sigmoid(x float64) float64 {
    return 1.0 / (1.0 + exp(-x))
}
func sigmoidDerivative(x float64) float64 {
    return x * (1.0 - x)
}

func exp(x float64) float64 {
    return 1.0 + x + (x*x)/2.0 + (x*x*x)/6.0 + (x*x*x*x)/24.0
}

outputerrors[k] =  (targets[k] - nn.outputlayer[k])

outputErrors[k] = -(targets[k] - nn.OutputLayer[k])