我的神经网络不起作用 [XOR 问题]

我正在尝试制作一个用于解决XOR问题的神经网络。但我做不到。总是给出错误的结果。也许我在你的数学上犯了一个错误。网络无法学习。结果总是相似的。

我没有使用BIAS。

Note: execute function = (feed-forward + backpropagation)
ALPHA = 0.5

这是代码：

//main.cpp

#include <iostream>
#include "neural_network.h"
int main(int argc, char const *argv[])
{
srand(time(NULL));
double array[][3] = {{0.0, 0.0, 0.0},
{0.0, 1.0, 1.0},
{1.0, 0.0, 1.0},
{1.0, 1.0, 0.0}};

neural_network* nn = new neural_network(3, 2, 2, 1, 1.0);
nn->create_network();
for(int i = 0; i < 15000; i++)
{
int index = rand() % 4;
#if DEBUG
std::cout<<"Inputs :"<<array[index][0]<<" , "<<array[index][1]<<std::endl;
std::cout<<"Outputs :"<<array[index][2]<<std::endl;
#endif    
nn->execute(array[index], &array[index][2]);
}
nn->print_weight();
nn->execute(array[0], &array[0][2]);
nn->print_output();
nn->execute(array[1], &array[1][2]);
nn->print_output();
nn->execute(array[2], &array[2][2]);
nn->print_output();
nn->execute(array[3], &array[3][2]);
nn->print_output();
return 0;
}

//前馈函数

void neural_network::feed_forward(double* inputs)
{
int index = 0;
for(int i = 0; i < neural_network::input_layer_size; i++)
neural_network::input_neuron[i] = inputs[i];
for(int i = 0; i < neural_network::hidden_layer_size; i++)
{
for(int j = 0; j < neural_network::input_layer_size; j++)
{
neural_network::hidden_neuron[i] += neural_network::input_neuron[j] * weight_I_H[index++];
}
neural_network::hidden_neuron[i] = neural_network::activation_func(neural_network::hidden_neuron[i]);
}    
index = 0;
for(int i = 0; i < neural_network::output_layer_size; i++)
{
for(int j = 0; j < neural_network::hidden_layer_size; j++)
{
neural_network::output_neuron[i] += neural_network::hidden_neuron[j] * weight_H_O[index++];
}
neural_network::output_neuron[i] = neural_network::activation_func(neural_network::output_neuron[i]);
}

}

//反向传播函数

void neural_network::back_propagation(double* outputs)
{
int index;
for(int i = 0; i < neural_network::output_layer_size; i++)
neural_network::err_output[i] = (outputs[i] - neural_network::output_neuron[i]);
for(int i = 0; i < neural_network::hidden_layer_size; i++)
{
index = i;
for(int j = 0; j < neural_network::output_layer_size; j++)
{
neural_network::err_hidden[i] += neural_network::weight_H_O[index] * neural_network::err_output[j] * neural_network::derivative_act_func(neural_network::output_neuron[j]);
neural_network::weight_H_O[index] += ALPHA * neural_network::err_output[j] * neural_network::derivative_act_func(neural_network::output_neuron[j]) * neural_network::hidden_neuron[i];
index += neural_network::hidden_layer_size;
}
}
for(int i = 0; i < neural_network::input_layer_size; i++)
{
index = i;
for(int j = 0; j < neural_network::hidden_layer_size; j++)
{
neural_network::weight_I_H[index] += ALPHA * neural_network::err_hidden[j] * neural_network::derivative_act_func(neural_network::hidden_neuron[j]) * neural_network::input_neuron[i];
index += neural_network::input_layer_size;
}
}
}

//输出

Input To Hidden :
H-1 :
Weight :-13.269
Weight :-13.2705
H-2 :
Weight :-12.5172
Weight :-12.5195
Hidden To Output :
O-1 :
Weight :-5.37707
Weight :-2.93218

Outputs for (0,0):
O-1 :0.0294265
Outputs for (0,1):
O-1 :0.507348
Outputs for (1,0):
O-1 :0.62418
Outputs for (1,1):
O-1 :0.651169