/******************************************************************************* * nn.c 1.0 © JOHN BULLINARIA 2004 * *******************************************************************************/ /* To compile use "cc nn.c -O -lm -o nn" and then run using "./nn" */ /* For explanations see: http://www.cs.bham.ac.uk/~jxb/NN/nn.html */ #include #include #include #include #include #define NUMPAT 4 #define NUMIN 2 #define NUMHID 2 #define NUMOUT 1 #define rando() ((double)rand()/((double)RAND_MAX+1)) main() { int i, j, k, p, np, op, ranpat[NUMPAT+1], epoch; int NumPattern = NUMPAT, NumInput = NUMIN, NumHidden = NUMHID, NumOutput = NUMOUT; double Input[NUMPAT+1][NUMIN+1] = { 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1 }; double Target[NUMPAT+1][NUMOUT+1] = { 0, 0, 0, 0, 0, 1, 0, 1, 0, 0 }; double SumH[NUMPAT+1][NUMHID+1], WeightIH[NUMIN+1][NUMHID+1], Hidden[NUMPAT+1][NUMHID+1]; double SumO[NUMPAT+1][NUMOUT+1], WeightHO[NUMHID+1][NUMOUT+1], Output[NUMPAT+1][NUMOUT+1]; double DeltaO[NUMOUT+1], SumDOW[NUMHID+1], DeltaH[NUMHID+1]; double DeltaWeightIH[NUMIN+1][NUMHID+1], DeltaWeightHO[NUMHID+1][NUMOUT+1]; double Error, eta = 0.5, alpha = 0.9, smallwt = 0.5; for( j = 1 ; j <= NumHidden ; j++ ) { /* initialize WeightIH and DeltaWeightIH */ for( i = 0 ; i <= NumInput ; i++ ) { DeltaWeightIH[i][j] = 0.0 ; WeightIH[i][j] = 2.0 * ( rando() - 0.5 ) * smallwt ; } } for( k = 1 ; k <= NumOutput ; k ++ ) { /* initialize WeightHO and DeltaWeightHO */ for( j = 0 ; j <= NumHidden ; j++ ) { DeltaWeightHO[j][k] = 0.0 ; WeightHO[j][k] = 2.0 * ( rando() - 0.5 ) * smallwt ; } } for( epoch = 0 ; epoch < 100000 ; epoch++) { /* iterate weight updates */ for( p = 1 ; p <= NumPattern ; p++ ) { /* randomize order of individuals */ ranpat[p] = p ; } for( p = 1 ; p <= NumPattern ; p++) { np = p + rando() * ( NumPattern + 1 - p ) ; op = ranpat[p] ; ranpat[p] = ranpat[np] ; ranpat[np] = op ; } Error = 0.0 ; for( np = 1 ; np <= NumPattern ; np++ ) { /* repeat for all the training patterns */ p = ranpat[np]; for( j = 1 ; j <= NumHidden ; j++ ) { /* compute hidden unit activations */ SumH[p][j] = WeightIH[0][j] ; for( i = 1 ; i <= NumInput ; i++ ) { SumH[p][j] += Input[p][i] * WeightIH[i][j] ; } Hidden[p][j] = 1.0/(1.0 + exp(-SumH[p][j])) ; } for( k = 1 ; k <= NumOutput ; k++ ) { /* compute output unit activations and errors */ SumO[p][k] = WeightHO[0][k] ; for( j = 1 ; j <= NumHidden ; j++ ) { SumO[p][k] += Hidden[p][j] * WeightHO[j][k] ; } Output[p][k] = 1.0/(1.0 + exp(-SumO[p][k])) ; /* Sigmoidal Outputs */ /* Output[p][k] = SumO[p][k]; Linear Outputs */ Error += 0.5 * (Target[p][k] - Output[p][k]) * (Target[p][k] - Output[p][k]) ; /* SSE */ /* Error -= ( Target[p][k] * log( Output[p][k] ) + ( 1.0 - Target[p][k] ) * log( 1.0 - Output[p][k] ) ) ; Cross-Entropy Error */ DeltaO[k] = (Target[p][k] - Output[p][k]) * Output[p][k] * (1.0 - Output[p][k]) ; /* Sigmoidal Outputs, SSE */ /* DeltaO[k] = Target[p][k] - Output[p][k]; Sigmoidal Outputs, Cross-Entropy Error */ /* DeltaO[k] = Target[p][k] - Output[p][k]; Linear Outputs, SSE */ } for( j = 1 ; j <= NumHidden ; j++ ) { /* 'back-propagate' errors to hidden layer */ SumDOW[j] = 0.0 ; for( k = 1 ; k <= NumOutput ; k++ ) { SumDOW[j] += WeightHO[j][k] * DeltaO[k] ; } DeltaH[j] = SumDOW[j] * Hidden[p][j] * (1.0 - Hidden[p][j]) ; } for( j = 1 ; j <= NumHidden ; j++ ) { /* update weights WeightIH */ DeltaWeightIH[0][j] = eta * DeltaH[j] + alpha * DeltaWeightIH[0][j] ; WeightIH[0][j] += DeltaWeightIH[0][j] ; for( i = 1 ; i <= NumInput ; i++ ) { DeltaWeightIH[i][j] = eta * Input[p][i] * DeltaH[j] + alpha * DeltaWeightIH[i][j]; WeightIH[i][j] += DeltaWeightIH[i][j] ; } } for( k = 1 ; k <= NumOutput ; k ++ ) { /* update weights WeightHO */ DeltaWeightHO[0][k] = eta * DeltaO[k] + alpha * DeltaWeightHO[0][k] ; WeightHO[0][k] += DeltaWeightHO[0][k] ; for( j = 1 ; j <= NumHidden ; j++ ) { DeltaWeightHO[j][k] = eta * Hidden[p][j] * DeltaO[k] + alpha * DeltaWeightHO[j][k] ; WeightHO[j][k] += DeltaWeightHO[j][k] ; } } } if( epoch%100 == 0 ) fprintf(stdout, "\nEpoch %-5d : Error = %f", epoch, Error) ; if( Error < 0.0004 ) break ; /* stop learning when 'near enough' */ } fprintf(stdout, "\n\nNETWORK DATA - EPOCH %d\n\nPat\t", epoch) ; /* print network outputs */ for( i = 1 ; i <= NumInput ; i++ ) { fprintf(stdout, "Input%-4d\t", i) ; } for( k = 1 ; k <= NumOutput ; k++ ) { fprintf(stdout, "Target%-4d\tOutput%-4d\t", k, k) ; } for( p = 1 ; p <= NumPattern ; p++ ) { fprintf(stdout, "\n%d\t", p) ; for( i = 1 ; i <= NumInput ; i++ ) { fprintf(stdout, "%f\t", Input[p][i]) ; } for( k = 1 ; k <= NumOutput ; k++ ) { fprintf(stdout, "%f\t%f\t", Target[p][k], Output[p][k]) ; } } fprintf(stdout, "\n\nGoodbye!\n\n") ; return 1 ; } /*******************************************************************************/