Code:
/******************************************************************
ArduinoANN - An artificial neural network for the Arduino
All basic settings can be controlled via the Network Configuration
section.
See robotics.hobbizine.com/arduinoann.html for details.
******************************************************************/
#include <math.h>
/******************************************************************
Network Configuration - customized per network
******************************************************************/
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 }
};
/******************************************************************
End Network Configuration
******************************************************************/
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];
void setup() {
Serial.begin(9600);
randomSeed(analogRead(3));
ReportEvery1000 = 1;
for ( p = 0 ; p < PatternCount ; p++ )
{
RandomizedIndex[p] = p ;
}
}
void loop ()
{
/******************************************************************
Initialize HiddenWeights and ChangeHiddenWeights
******************************************************************/
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 ;
}
}
/******************************************************************
Initialize OutputWeights and ChangeOutputWeights
******************************************************************/
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 ;
}
}
Serial.println("Initial/Untrained Outputs: ");
toTerminal();
/******************************************************************
Begin training
******************************************************************/
for ( TrainingCycle = 1 ; TrainingCycle < 2147483647 ; TrainingCycle++)
{
/******************************************************************
Randomize order of training patterns
******************************************************************/
for ( p = 0 ; p < PatternCount ; p++)
{
q = random(PatternCount);
r = RandomizedIndex[p] ;
RandomizedIndex[p] = RandomizedIndex[q] ;
RandomizedIndex[q] = r ;
}
Error = 0.0 ;
/******************************************************************
Cycle through each training pattern in the randomized order
******************************************************************/
for ( q = 0 ; q < PatternCount ; q++ )
{
p = RandomizedIndex[q];
/******************************************************************
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)) ;
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]) ;
}
/******************************************************************
Backpropagate errors to hidden layer
******************************************************************/
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]) ;
}
/******************************************************************
Update Inner-->Hidden Weights
******************************************************************/
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] ;
}
}
/******************************************************************
Update Hidden-->Output Weights
******************************************************************/
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] ;
}
}
}
/******************************************************************
Every 1000 cycles send data to terminal for display
******************************************************************/
ReportEvery1000 = ReportEvery1000 - 1;
if (ReportEvery1000 == 0)
{
Serial.println();
Serial.println();
Serial.print ("TrainingCycle: ");
Serial.print (TrainingCycle);
Serial.print (" Error = ");
Serial.println (Error, 5);
toTerminal();
if (TrainingCycle == 1)
{
ReportEvery1000 = 999;
}
else
{
ReportEvery1000 = 1000;
}
}
/******************************************************************
If error rate is less than pre-determined threshold then end
******************************************************************/
if ( Error < Success ) break ;
}
Serial.println ();
Serial.println();
Serial.print ("TrainingCycle: ");
Serial.print (TrainingCycle);
Serial.print (" Error = ");
Serial.println (Error, 5);
toTerminal();
Serial.println ();
Serial.println ();
Serial.println ("Training Set Solved! ");
Serial.println ("--------");
Serial.println ();
Serial.println ();
ReportEvery1000 = 1;
}
void toTerminal()
{
for ( p = 0 ; p < PatternCount ; p++ )
{
Serial.println();
Serial.print (" Training Pattern: ");
Serial.println (p);
Serial.print (" Input ");
for ( i = 0 ; i < InputNodes ; i++ )
{
Serial.print (Input[p][i], DEC);
Serial.print (" ");
}
Serial.print (" Target ");
for ( i = 0 ; i < OutputNodes ; i++ )
{
Serial.print (Target[p][i], 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)) ;
}
Serial.print (" Output ");
for ( i = 0 ; i < OutputNodes ; i++ )
{
Serial.print (Output[i], 5);
Serial.print (" ");
}
}
}
unter "anleitung" in den einstellungen etwas verändern und lernen wie ich diese verändern muss um ein bestimmtes ergebnis zu erreichen. Und vielleicht noch der hinweis, in welchem codeteil das passiert, wäre aber schon unter nice to have zu sehen...
Zitieren
Zitat von HaWe
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