machine learning ANN perlu bantuan

Dependencies:   mbed

main.cpp

Committer:
asyrofi
Date:
13 months ago
Revision:
0:482cc6c25690

File content as of revision 0:482cc6c25690:

#include <math.h>
#include <mbed.h>
Serial pc(USBTX, USBRX);
const int PatternCount = 10;
const int InputNodes = 7;
const int HiddenNodes = 8;
const int OutputNodes = 4;
const float LearningRate = 0.3;
const float Momentum = 0.9;
const float InitialWeightMax = 0.5;
const float Success = 0.0004;

const byte Input[PatternCount][InputNodes] = {
  { 1, 1, 1, 1, 1, 1, 0 },  // 0
  { 0, 1, 1, 0, 0, 0, 0 },  // 1
  { 1, 1, 0, 1, 1, 0, 1 },  // 2
  { 1, 1, 1, 1, 0, 0, 1 },  // 3
  { 0, 1, 1, 0, 0, 1, 1 },  // 4
  { 1, 0, 1, 1, 0, 1, 1 },  // 5
  { 0, 0, 1, 1, 1, 1, 1 },  // 6
  { 1, 1, 1, 0, 0, 0, 0 },  // 7 
  { 1, 1, 1, 1, 1, 1, 1 },  // 8
  { 1, 1, 1, 0, 0, 1, 1 }   // 9
}; 

const byte Target[PatternCount][OutputNodes] = {
  { 0, 0, 0, 0 },  
  { 0, 0, 0, 1 }, 
  { 0, 0, 1, 0 }, 
  { 0, 0, 1, 1 }, 
  { 0, 1, 0, 0 }, 
  { 0, 1, 0, 1 }, 
  { 0, 1, 1, 0 }, 
  { 0, 1, 1, 1 }, 
  { 1, 0, 0, 0 }, 
  { 1, 0, 0, 1 } 
};

int i, j, p, q, r;
int ReportEvery1000;
int RandomizedIndex[PatternCount];
long  TrainingCycle;
float Rando;
float Error;
float Accum;


float Hidden[HiddenNodes];
float Output[OutputNodes];
float HiddenWeights[InputNodes+1][HiddenNodes];
float OutputWeights[HiddenNodes+1][OutputNodes];
float HiddenDelta[HiddenNodes];
float OutputDelta[OutputNodes];
float ChangeHiddenWeights[InputNodes+1][HiddenNodes];
float ChangeOutputWeights[HiddenNodes+1][OutputNodes];

int main()
{
  pc.baud(9600);
  randomSeed(analogRead(3));
  ReportEvery1000 = 1;
  for( p = 0 ; p < PatternCount ; p++ ) 
  {    
    RandomizedIndex[p] = p ;
  }
  
  void loop ()
  {



    for( i = 0 ; i < HiddenNodes ; i++ ) 
    {    
        for( j = 0 ; j <= InputNodes ; j++ ) 
        { 
            ChangeHiddenWeights[j][i] = 0.0 ;
            Rando = float(random(100))/100;
            HiddenWeights[j][i] = 2.0 * ( Rando - 0.5 ) * InitialWeightMax ;
        }
    }

    for( i = 0 ; i < OutputNodes ; i ++ ) 
    {    
        for( j = 0 ; j <= HiddenNodes ; j++ ) 
        {
            ChangeOutputWeights[j][i] = 0.0 ;  
            Rando = float(random(100))/100;        
            OutputWeights[j][i] = 2.0 * ( Rando - 0.5 ) * InitialWeightMax ;
        }
    }
    pc.printf("Initial/Untrained Outputs: \n");
    toTerminal();

    for( TrainingCycle = 1 ; TrainingCycle < 2147483647 ; TrainingCycle++) 
    {    

        for( p = 0 ; p < PatternCount ; p++) 
        {
            q = random(PatternCount);
            r = RandomizedIndex[p] ; 
            RandomizedIndex[p] = RandomizedIndex[q] ; 
            RandomizedIndex[q] = r ;
        }
        Error = 0.0 ;

        for( q = 0 ; q < PatternCount ; q++ ) 
        {    
            p = RandomizedIndex[q];

            for( i = 0 ; i < HiddenNodes ; i++ ) 
            {    
                Accum = HiddenWeights[InputNodes][i] ;
                for( j = 0 ; j < InputNodes ; j++ ) 
                {
                    Accum += Input[p][j] * HiddenWeights[j][i] ;
                }
                Hidden[i] = 1.0/(1.0 + exp(-Accum)) ;
            }


            for( i = 0 ; i < OutputNodes ; i++ ) 
            {    
                Accum = OutputWeights[HiddenNodes][i] ;
                for( j = 0 ; j < HiddenNodes ; j++ ) 
                {
                    Accum += Hidden[j] * OutputWeights[j][i] ;
                }
                Output[i] = 1.0/(1.0 + exp(-Accum)) ;   
                OutputDelta[i] = (Target[p][i] - Output[i]) * Output[i] * (1.0 - Output[i]) ;   
                Error += 0.5 * (Target[p][i] - Output[i]) * (Target[p][i] - Output[i]) ;
            }


            for( i = 0 ; i < HiddenNodes ; i++ ) 
            {    
                Accum = 0.0 ;
                for( j = 0 ; j < OutputNodes ; j++ ) 
                {
                    Accum += OutputWeights[i][j] * OutputDelta[j] ;
                }
                HiddenDelta[i] = Accum * Hidden[i] * (1.0 - Hidden[i]) ;
            }




            for( i = 0 ; i < HiddenNodes ; i++ ) 
            {     
                ChangeHiddenWeights[InputNodes][i] = LearningRate * HiddenDelta[i] + Momentum * ChangeHiddenWeights[InputNodes][i] ;
                HiddenWeights[InputNodes][i] += ChangeHiddenWeights[InputNodes][i] ;
                for( j = 0 ; j < InputNodes ; j++ ) 
                { 
                    ChangeHiddenWeights[j][i] = LearningRate * Input[p][j] * HiddenDelta[i] + Momentum * ChangeHiddenWeights[j][i];
                    HiddenWeights[j][i] += ChangeHiddenWeights[j][i] ;
                }
            }


            for( i = 0 ; i < OutputNodes ; i ++ ) 
            {    
                ChangeOutputWeights[HiddenNodes][i] = LearningRate * OutputDelta[i] + Momentum * ChangeOutputWeights[HiddenNodes][i] ;
                OutputWeights[HiddenNodes][i] += ChangeOutputWeights[HiddenNodes][i] ;
                for( j = 0 ; j < HiddenNodes ; j++ ) 
                {
                    ChangeOutputWeights[j][i] = LearningRate * Hidden[j] * OutputDelta[i] + Momentum * ChangeOutputWeights[j][i] ;
                    OutputWeights[j][i] += ChangeOutputWeights[j][i] ;
                }
            }
     }


    ReportEvery1000 = ReportEvery1000 - 1;
    if (ReportEvery1000 == 0)
    { 
      pc.printf ("TrainingCycle: ");
      pc.printf ("%i",(int16_t)TrainingCycle);
      pc.printf ("  Error = ");
      pc.printf ("%i\n",(int16_t)Error, 5);

      toTerminal();

      if (TrainingCycle==1)
      {
        ReportEvery1000 = 999;
      }
        else
      {
        ReportEvery1000 = 1000;
      }
    }    

    if( Error < Success ) break ;  
  } 
    pc.printf ("TrainingCycle: ");
    pc.printf ("%i",(int16_t)TrainingCycle);
    pc.printf ("  Error = ");
    pc.printf ("%i\n",(int16_t)Error, 5);

    toTerminal();

    pc.printf ("Training Set Solved!\n");
    pc.printf ("--------\n");   
    ReportEvery1000 = 1;
    }
}  

void toTerminal()
{

  for( p = 0 ; p < PatternCount ; p++ ) { 
    pc.printf ("  Training Pattern: ");
    pc.printf ("%i\n",(int16_t)p);      
    pc.printf ("  Input ");
    for( i = 0 ; i < InputNodes ; i++ ) {
      pc.printf ("%i,%i",(int16_t)Input[p][i],(int16_t)DEC);
      pc.printft (" ");
    }
    pc.printf ("  Target ");
    for( i = 0 ; i < OutputNodes ; i++ ) {
      pc.printf ("%i,%i",(int16_t)Input[p][i],(int16_t)DEC);
      Serial.print (" ");
    }
/******************************************************************
* Compute hidden layer activations
******************************************************************/

    for( i = 0 ; i < HiddenNodes ; i++ ) {    
      Accum = HiddenWeights[InputNodes][i] ;
      for( j = 0 ; j < InputNodes ; j++ ) {
        Accum += Input[p][j] * HiddenWeights[j][i] ;
      }
      Hidden[i] = 1.0/(1.0 + exp(-Accum)) ;
    }

/******************************************************************
* Compute output layer activations and calculate errors
******************************************************************/

    for( i = 0 ; i < OutputNodes ; i++ ) {    
      Accum = OutputWeights[HiddenNodes][i] ;
      for( j = 0 ; j < HiddenNodes ; j++ ) {
        Accum += Hidden[j] * OutputWeights[j][i] ;
      }
      Output[i] = 1.0/(1.0 + exp(-Accum)) ; 
    }
    pc.printf ("  Output ");
    for( i = 0 ; i < OutputNodes ; i++ ) {       
      pc.printf ("%i",Output[i], 5);
      pc.printf (" ");
    }
  }


}