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Single neuron in neural network using c++ – Artificial Intelligence

I am trying to write a simple neuron function in c++ similar to this image. I am using sigmoid function as activation function.
enter image description here

This is my c++ neuron function

#include<math.h>

double neuron(double layer_inputs[],int iter)
{

  // Feed forwarding single neuron
    double network=0;
    double bias=1;
    double activation;

    //get number of elements in the layer
    const int num=sizeof(layer_inputs)/sizeof(layer_inputs[0]);
    double weight[num];

    for ( int i = 0; i < num; i++ )
    {
        if(iter==0)
        {
        //first time assigning random weights
            weight[i]=rand();
        }

        //feed forwarding summation
        network=network+(layer_inputs[i]*weight[i]+bias);


    }

    activation= 1.0 / (1.0 + exp(-network)); //sigmoid activation function

return activation;
}

The problem is I dont know whether I made any logical error in my code. iter is the iteration variable to check if the neuron is activating for first time. Is my writing the neuron in a neural network correct.

EDIT:

Even though not from a programmatic or quant background I am fascinated about programming,neural networks and artificial intelligence. I hve used inbuilt functions in caret R but for more understanding I want to create a simple neural network from scratch. I learned most of the basics from internet and I am posting my codes here because I am sure I made some illogical but executing script.

#include<iostream>
#include <math.h>//pow, exp
#include "sqrtnn.h" //neuron()


int main()
{

    double input[]= {1,4,9,16,25,36,49,64,81,100};
    double output[]= {1,2,3,4,5,6,7,8,9,10};
    //number of layers
    double layer=3;
    double output_network[10];
    double error[10];
    double learning_rate=0.02;
    //number of iterations
    int iter=10;
    int input_num=sizeof(input)/sizeof(input[0]);
    std::cout<<"Simple  neural network for solving square rootn nINPUT -> OUTPUTnn";
    for ( int i = 0; i < iter; i++ )
    {
        for ( int j = 0; j < input_num; j++ )
        {
            for ( int k = 0; k < layer; k++ )
            {
                //feed forwarding
                output_network[j] =neuron(input,i) ; //sigmoid activation function

                //back propogation
                error[j]=1/2*pow(output[j]-output_network[j],2);//error function
                std::cout<<input[j]<<" -> "<< output[j]<<"= "<< error[j] <<"n";


            }
        }
    }


    return 0;
}
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2

Answers


  1. 0

    You’re writing C code, not C++. C arrays don’t know their own size. Use std::vector<double> layer_inputs so you can call layer_inputs.size().

    Other C bits in your code: don’t declare variables until you need them; you have declared activation far too early. In fact, I wouldn’t define it at all – just return 1.0 / (1.0 + exp(-network));.

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  2. 0

    here is the problem just convert double input[10] to double input[]because it is c++ not c do this on output array also and it is not a training neural network model because actual (ready maid inputs/outputs) are provided and double learning_rate = 0.02; is declared not used anywhere.

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