rakha asyrofi
/
ANN_Coba
machine learning ANN perlu bantuan
main.cpp
- Committer:
- asyrofi
- Date:
- 2018-04-14
- 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 (" "); } } }