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simple-net.c
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simple-net.c
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/*
* simple-net.c
* Simple Neural Network
* Stephen Cook
* 10/12/2008
*
* Implement a back-propagation neural network with verious network configurations.
*
*/
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <time.h>
#define FALSE 0
#define TRUE 1
#define LOW 0.00
#define HIGH 1.00
#define BIAS 1.00
#define MAX_DOUBLE +HUGE_VAL
#define MIN_DOUBLE -HUGE_VAL
#define sqr(x) ((x)*(x))
#define INPUT_FILENAME "quadrant_data.csv"
#define TEST_DATA_FILE "quadrant_test_data.csv"
#define NUM_INPUT_VECTORS 800
#define MAX_EPOCHS 1000
#define MIN_ERROR 0.0050
/* Network Configuration Settings */
#define NUM_LAYERS 5
#define N 2 /* Number of input nodes */
#define M 4 /* Number of output nodes */
//int Nodes[NUM_LAYERS] = {N, 4, 25, 50, M};
//int Nodes[NUM_LAYERS] = {N, 4, 3, M};
int Nodes[NUM_LAYERS] = {N, 4, M};
#define ETA 0.189
#define ALPHA 0.1
#define GAIN 1.0
/* Structures */
typedef struct {
int Nodes;
double* Output;
double* Error;
double** Weight;
double** WeightSave;
double** dWeight;
} LAYER;
typedef struct {
LAYER** Layer;
LAYER* InputLayer;
LAYER* OutputLayer;
double Alpha;
double Eta;
double Gain;
double Error;
double EpochError;
double TestEpochError;
} NET;
/* Global Variables */
int i; // General Increments and record keeping
int incr[NUM_INPUT_VECTORS+1][2];
double tr[NUM_INPUT_VECTORS+1][3];
int tstincr[NUM_INPUT_VECTORS+1][2]; // Test Increment Data
double tsttr[NUM_INPUT_VECTORS+1][3]; // Test Data
FILE *file;
double genRandom(double Low, double High) {
return ((double) rand() / RAND_MAX) * (High-Low) + Low;
}
/* *************************** genNetwork - Initialize Network and Layers ******************************* */
void genNetwork(NET *Net) {
int l;
Net->Layer= (LAYER**) calloc(NUM_LAYERS+1, sizeof(LAYER*));
for(l=0;l<NUM_LAYERS;l++) {
Net->Layer[l] = (LAYER*) malloc(sizeof(LAYER));
Net->Layer[l]->Nodes = Nodes[l];
Net->Layer[l]->Output = (double*) calloc(Nodes[l]+1, sizeof(double));
Net->Layer[l]->Error = (double*) calloc(Nodes[l]+1, sizeof(double));
Net->Layer[l]->Weight = (double**) calloc(Nodes[l]+1, sizeof(double*));
Net->Layer[l]->WeightSave = (double**) calloc(Nodes[l]+1, sizeof(double*));
Net->Layer[l]->dWeight = (double**) calloc(Nodes[l]+1, sizeof(double*));
Net->Layer[l]->Output[0] = BIAS;
if (l>0) {
for (i=1; i<=Nodes[l]; i++) {
Net->Layer[l]->Weight[i] = (double*) calloc(Nodes[l-1]+2, sizeof(double));
Net->Layer[l]->WeightSave[i] = (double*) calloc(Nodes[l-1]+2, sizeof(double));
Net->Layer[l]->dWeight[i] = (double*) calloc(Nodes[l-1]+2, sizeof(double));
}
}
}
Net->InputLayer = Net->Layer[0];
Net->OutputLayer = Net->Layer[NUM_LAYERS - 1];
Net->Alpha = ALPHA;
Net->Eta = ETA;
Net->Gain = GAIN;
}
void genRandomWeights(NET* Net) {
int l,j;
// Could change high/low to be based on heuristic in book - based on nodes, layers, etc.
for (l=1; l<NUM_LAYERS; l++) {
for (i=1; i<=Net->Layer[l]->Nodes; i++) {
for (j=0; j<=Net->Layer[l-1]->Nodes; j++) {
Net->Layer[l]->Weight[i][j] = genRandom(-0.5000, 0.5000); // low and high random numbers.
}
}
}
}
void setInput(NET* Net, double* Input) {
//printf("Setting Input: \t");
for (i=1; i<=Net->InputLayer->Nodes; i++) {
Net->InputLayer->Output[i] = Input[i-1];
//printf(" %lf ",Net->InputLayer->Output[i]);
}
//printf("\n");
}
void getOutput(NET* Net, double* Output) {
for (i=1; i<=Net->OutputLayer->Nodes; i++) {
Output[i-1] = Net->OutputLayer->Output[i];
}
}
void layerPropagate(NET* Net, LAYER* Lower, LAYER* Upper) {
int j,k;
double Sum;
for (k=1; k<=Upper->Nodes; k++) {
Sum = 0;
for (j=0; j<=Lower->Nodes; j++) {
Sum += Upper->Weight[k][j] * Lower->Output[j];
}
Upper->Output[k] = 1 / (1 + exp(-Net->Gain * Sum));
//printf(" SUM(%d,%d)=%lf\tOutput[%d]=%lf \n",k,j,Sum,k,Upper->Output[k]);
}
}
void netPropagate(NET* Net) {
int l;
for (l=0; l<NUM_LAYERS-1; l++) {
//printf("Propagating Layer %d\n",l);
layerPropagate(Net, Net->Layer[l], Net->Layer[l+1]);
}
}
void calcOutputError(NET* Net, double* Target) {
int j;
double Out, Err;
Net->Error = 0;
for (j=1; j<=Net->OutputLayer->Nodes; j++) {
Out = Net->OutputLayer->Output[j];
Err = Target[j-1]-Out;
Net->OutputLayer->Error[j] = Net->Gain * Out * (1-Out) * Err;
Net->Error += 0.5 * sqr(Err);
}
}
void layerBackprop(NET* Net, LAYER* Upper, LAYER* Lower) {
int k,j;
double Out, Err;
for (k=1; k<=Lower->Nodes; k++) {
Out = Lower->Output[k];
Err = 0;
for (j=1; j<=Upper->Nodes; j++) {
Err += Upper->Weight[j][k] * Upper->Error[j];
}
Lower->Error[k] = Net->Gain * Out * (1-Out) * Err;
}
}
void netBackprop(NET* Net) {
int l;
for (l=NUM_LAYERS-1; l>1; l--) {
layerBackprop(Net, Net->Layer[l], Net->Layer[l-1]);
}
}
void updateWeights(NET* Net) {
int l,k,j;
double Out, Err, dWeight;
for (l=1; l<NUM_LAYERS; l++) {
for (k=1; k<=Net->Layer[l]->Nodes; k++) {
for (j=0; j<=Net->Layer[l-1]->Nodes; j++) {
Out = Net->Layer[l-1]->Output[j];
Err = Net->Layer[l]->Error[k];
dWeight = Net->Layer[l]->dWeight[k][j];
Net->Layer[l]->Weight[k][j] += Net->Eta * Err * Out + Net->Alpha * dWeight;
Net->Layer[l]->dWeight[k][j] = Net->Eta * Err * Out;
}
}
}
}
void netRun(NET* Net, double* Input, double* Target, int Training) {
double* Output;
Output = (double*) calloc(M+1, sizeof(double));
setInput(Net, Input);
netPropagate(Net);
getOutput(Net, Output);
calcOutputError(Net, Target);
//printf("Target:{ %lf %lf %lf %lf } => Error: %lf\n",Target[0],Target[1],Target[2],Target[3],Net->Error);
if (Training) {
netBackprop(Net);
updateWeights(Net);
}
}
void netTrain(NET* Net, int Epochs) {
int j,n,l;
//double Output[M];
double Target[M];
double tstTarget[M];
for(l=0;l<Epochs;l++) {
//printf("Epoch: %d\n",l);
Net->EpochError=0.0;
Net->TestEpochError=0.0;
for (n=0; n<NUM_INPUT_VECTORS; n++) {
for(j=0;j<M;j++) {
Target[j]=0.000;
tstTarget[j]=0.000;
}
if(incr[n][0]==1) { Target[0]=1.00; } else if(incr[n][0]==2) { Target[1]=1.00; }
else if(incr[n][0]==3) { Target[2]=1.00; } else if(incr[n][0]==4) { Target[3]=1.00; }
//printf("%d %d { %lf %lf %lf %lf }\n",n,incr[n][0],Target[0],Target[1],Target[2],Target[3]);
netRun(Net, tr[n], Target, TRUE);
Net->EpochError+=Net->Error;
if(tstincr[n][0]==1) { tstTarget[0]=1.00; } else if(tstincr[n][0]==2) { tstTarget[1]=1.00; }
else if(tstincr[n][0]==3) { tstTarget[2]=1.00; } else if(tstincr[n][0]==4) { tstTarget[3]=1.00; }
netRun(Net, tsttr[n], tstTarget, FALSE);
Net->TestEpochError+=Net->Error;
}
//printf("\t EpochError: %f \t TestError: %lf\n",Net->EpochError,Net->TestEpochError);
}
}
/* ********************************************* MAIN ************************************************ */
int main(int argc, char *argv[]) {
char line[80];
NET Net;
int Stop;
int randomAr[NUM_INPUT_VECTORS+1],last,temp,randomNum;
int numEpoch;
double lastTrainingError,deltaErr;
int RandomizeTrainingSet=FALSE;
srand(time(0));
for(i=0;i<NUM_INPUT_VECTORS;i++) {
randomAr[i]=i;
}
/* Randomize the training data sets */
if(RandomizeTrainingSet) {
for(last=NUM_INPUT_VECTORS;last>1;last--) {
randomNum=rand()%last;
temp=randomAr[randomNum];
randomAr[randomNum]=randomAr[last - 1];
randomAr[last - 1] = temp;
}
}
/* Read in the data from the file */
file = fopen(INPUT_FILENAME,"r");
for(i=0;i<NUM_INPUT_VECTORS;i++) {
if(fgets(line, sizeof line, file) == NULL) {
break;
}
temp=randomAr[i];
sscanf(line,"%d,%d,%lf,%lf",&incr[temp][0],&incr[temp][1],&tr[temp][0],&tr[temp][1]);
//sscanf(line,"%d,%d,%lf,%lf",&incr[i][0],&incr[i][1],&tr[i][0],&tr[i][1]);
}
fclose(file);
file = fopen(TEST_DATA_FILE,"r");
for(i=0;i<NUM_INPUT_VECTORS;i++) {
if(fgets(line, sizeof line, file) == NULL) {
break;
}
temp=randomAr[i];
sscanf(line,"%d,%d,%lf,%lf",&tstincr[temp][0],&tstincr[temp][1],&tsttr[temp][0],&tsttr[temp][1]);
}
fclose(file);
/*
for(i=0;i<NUM_INPUT_VECTORS;i++) {
printf("%d %d %lf %lf\n",tstincr[i][0],tstincr[i][1],tsttr[i][0],tsttr[i][1]);
}
*/
genNetwork(&Net); // Create the network
genRandomWeights(&Net); // Fill it with random weights
Stop = FALSE;
numEpoch=0;
lastTrainingError=100000.00;
/* Training Loop */
do {
netTrain(&Net, 1);
deltaErr=fabs(Net.EpochError - lastTrainingError);
if(deltaErr<MIN_ERROR || numEpoch>MAX_EPOCHS) Stop=TRUE;
printf("%d, %lf, %lf\n",numEpoch,(double)Net.EpochError/(double)NUM_INPUT_VECTORS,(double)Net.TestEpochError/(double)NUM_INPUT_VECTORS);
numEpoch++;
lastTrainingError=Net.EpochError;
} while(!Stop);
return(0);
}