Neural Network not learning. Stuck at 50% - c++

Question

I'm beginner in NNs. I'm trying to create a NN for XOR function but it's not learning, it's stuck at 50%
Can anyone give me some advice? Thanks.
Here's the code:
/// Matrix.cpp
#include "pch.h"
#include "Matrix.h"
....
Matrix Matrix::sigmoidDerivate(const Matrix &m) {
assert(m.rows >= 1 && m.cols >= 1);
Matrix tmp(m.rows, m.cols);
for (ushort i = 0; i < tmp.rows; i++) {
for (ushort j = 0; j < tmp.cols; j++) {
tmp.mat[i][j] = m.mat[i][j]*(1-m.mat[i][j]);
}
}
return tmp;
}
Matrix Matrix::sigmoid(const Matrix &m) {
assert(m.rows >= 1 && m.cols >= 1);
Matrix tmp(m.rows, m.cols);
for (ushort i = 0; i < tmp.rows; i++) {
for (ushort j = 0; j < tmp.cols; j++) {
tmp.mat[i][j]= 1 / (1 + exp(-m.mat[i][j]));
}
}
return tmp;
}
Matrix Matrix::randomMatrix(ushort rows, ushort cols) {
assert(rows>=1 && cols>=1);
Matrix tmp(rows,cols);
const int range_from = -3;
const int range_to = 3;
std::random_device rand_dev;
std::mt19937 generator(rand_dev());
std::uniform_real_distribution<double> distr(range_from, range_to);
for (ushort i = 0; i < rows; i++) {
for (ushort j = 0; j < cols; j++) {
tmp.mat[i][j] = distr(generator);
}
}
return tmp;
}
And this is main () :
vector<vector<double>> in = {
{0,0},
{1,0},
{0,1},
{1,1}
};
vector<double> out = { 0,1,1,0 };
const ushort inputNeurons = 2;
const ushort hiddenNeurons = 3;
const ushort outputNeurons = 1;
const double learningRate = 0.03;
Matrix w_0_1 = Matrix::randomMatrix(inputNeurons, hiddenNeurons);
Matrix w_1_2 = Matrix::randomMatrix(hiddenNeurons, outputNeurons);
unsigned int epochs = 100000;
for (int i = 0; i < epochs; i++) {
for (int j = 0; j < in.size(); j++) {
Matrix Layer_0 = Matrix::createRowMatrix(in[j]);
Matrix desired_output = Matrix::createRowMatrix({ out[j] });
Matrix Layer_1 = Matrix::sigmoid(Matrix::multiply(Layer_0, w_0_1));
Matrix Layer_2 = Matrix::sigmoid(Matrix::multiply(Layer_1, w_1_2));
Matrix error = Matrix::POW2(Matrix::substract(Layer_2, desired_output));
//backprop
Matrix Layer_2_delta = Matrix::elementWiseMultiply(
Matrix::substract(Layer_2, desired_output),
Matrix::sigmoidDerivate(Layer_2)
);
Matrix Layer_1_delta = Matrix::elementWiseMultiply(
Matrix::multiply(Layer_2_delta, Matrix::transpose(w_1_2)),
Matrix::sigmoidDerivate(Layer_1)
);
Matrix w_1_2_delta = Matrix::multiply(Matrix::transpose(Layer_1), Layer_2_delta);
Matrix w_0_1_delta = Matrix::multiply(Matrix::transpose(Layer_0), Layer_1_delta);
//updating weights
w_0_1 = Matrix::multiply(w_0_1_delta, learningRate);
w_1_2 = Matrix::multiply(w_1_2_delta, learningRate);
}
}
NN architecture is : 2 ->3 ->1
In hidden layer if number is small, like 2-4, the output is 50%. and for 8 neurons on hidden layer ..output becomes around 49%.
Some help please.

Answer 1

I'm not that into c++ so I'm not sure. But in the line:
Matrix::substract(Layer_2, desired_output),
You are doing something like subtracting the desired "good" output from the existing Layer. In my opinion that should be the other way round. So you have to multiply it by -1
For me it's working like that. If you like so I can send you my source code. (it's java)