New folder (2)/ANNneural.c
#include
#include
#include
#include
#include
// #include
// Main Sequence
void parseCommandInput(int, char *[]);
void initialize();
void openFiles();
void train();
void test();
void exportNeuralNetwork();
void createOutputGraph();
void userInput();
void func_closeFiles();
// Import
void importNN();
// I/O
void printNN();
void printState();
// Activation function declaration
void feedForrwardTanh(double x1, double x2, double * ans1, double * ans2);
void backpropagationTanh(int);
void feedForrwardReLU(double x1, double x2, double * ans1, double * ans2);
void backpropagationReLU(int);
void feedForrwardSigmoid(double x1, double x2, double * ans1, double * ans2);
void backpropagationSigmoid(int);
void feedForrwardDoubleSigmoid(double x1, double x2, double * ans1, double * ans2);
void backpropagationDoubleSigmoid(int);
void backproagationhwsolution();
// Math functions declaration
double sigmoid(double);
double doubleSigmoid(double);
double sigmoidDeri(double);
double double_SigmoidDeri(double);
int IMPORT = 0;
int RANDOM = 0;
int INTERACTION = 0;
int ACTIVATION = 0;
double ROUND = 0.1;
double E = 0.1;
int EPOCH = 1000;
int NUMEPOCH = 3;
int DEBUG =0;
float x1, x2;
float z1, z2;
float t1, t2;
float y10, y2, y3, y4;
float s1, s2, s3, s4;
float b1, b2, b3, b4;
float w11, w12, w21, w22;
float u11, u12, u21, u22;
float b10, b20, b30, b40;
float w110, w120, w210, w220;
float u110, u120, u210, u220;
float *X1, *X2, *T1, *T2;
FILE * net;
FILE * testExamples;
FILE * import;
char * importFile = "import.txt";
int main(int argc, char *argv[]) {
parseCommandInput(argc, argv);
initialize();
openFiles();
importNN();
train();
test();
exportNeuralNetwork();
createOutputGraph();
userInput();
func_closeFiles();
}
void parseCommandInput(int argc, char *argv[]) {
system("cls");
long seed = 0;
int opt = -1;
int tempEPOCH = 0;
if(argc > 1){
while ((opt = getopt(argc, argv, "ouipsra:l:f:e:n:")) != -1) {
switch(opt) {
case 'a':
ACTIVATION = atoi(optarg);
switch (ACTIVATION) {
case 0:
printf("Using activation TANH() (%d)\n", ACTIVATION);
break;
case 1:
printf("Using activation ReLU() (%d)\n", ACTIVATION);
break;
case 2:
printf("Using activation Sigmoid() (%d)\n", ACTIVATION);
break;
case 3:
printf("Using activation Double Sigmoid() (%d)\n", ACTIVATION);
break;
}
break;
case 'd':
DEBUG = 1;
printf("Settings DEBUG to %d\n", DEBUG);
break;
case 'u':
INTERACTION = 1;
printf("Settings INTERACTION to %d\n", INTERACTION);
break;
case 'r':
RANDOM = 1;
seed = time(NULL);
srand(seed);
printf("Using random seed: %d\n", seed);
break;
case 'n':
RANDOM = 1;
seed = atoi(optarg);
srand(seed);
printf("Using random seed: %d\n", seed);
break;
case 'i':
IMPORT = 1;
printf("Importing NN from %s\n", importFile);
break;
case 'f':
IMPORT = 1;
importFile = optarg;
printf("Importing NN from %s\n", importFile);
break;
case 'e':
EPOCH = atoi(optarg);
printf("Setting --- EPOCH --- to %d\n", EPOCH);
break;
case 'l':
E = atof(optarg);
printf("Setting LEARNING RATE to %g\n", E);
break;
case 'o':
ROUND = atof(optarg);
printf("Setting ROUND to %g\n", ROUND);
break;
}
}
} else {
printf("No options select: Running defaults\n");
printState();
}
}
void printState() {
int NUMEPOCH = 3;
printf("\n");
printf("Import: %d\n", IMPORT);
printf("Interaction: %d\n", INTERACTION);
printf("Activation: %d\n", ACTIVATION);
printf("Round: %g\n", ROUND);
printf("Learning Rate: %g\n", E);
printf("Epoch size: %d\n", EPOCH);
printf("Num Epochs: %d\n", NUMEPOCH - 1);
printf("\************************************************n");
printf("\import functions are disabled.\n");
printf("To set interaction to true use: -u\n");
printf("To set the round amount use: -o [AMOUNT]\n");
printf("To set learning rate use: -l [AMOUNT]\n");
printf("To set Epoch size use: -e [AMOUNT]\n");
printf("To change the activation function use: -a [option]\n");
printf("-a 0 for tanh\n");
printf("-a 1 for relu\n");
printf("-a 2 for sigmoid\n");
printf("-a 3 for double sigmoid\n");
printf("To get a new random seed use: -r\n");
printf("To use a custom random seed use: -n [SEED]\n");
printf("\n************************************************\n");
}
void initialize() {
// we are initialize the function
X1 = (float*) malloc(EPOCH * sizeof(float));
X2 = (float*) malloc(EPOCH * sizeof(float));
T1 = (float*) malloc(EPOCH * sizeof(float));
T2 = (float*) malloc(EPOCH * sizeof(float));
for (size_t i = 0; i < EPOCH; i++) {
X1[i] = (rand() % 2);
X2[i] = (rand() % 2);
T1[i] = (X1[i] == X2[i]) ? 0 : 1;
T2[i] = (X1[i] == 1 && X2[i] == 1) ? 1 : 0;
}
// b for propoation w :weight
b10 = ((rand() % 100) / 100.0) - 0.5;
b20 = ((rand() % 100) / 100.0) - 0.5;
b30 = ((rand() % 100) / 100.0) - 0.5;
b40 = ((rand() % 100) / 100.0) - 0.5;
w110 = ((rand() % 100) / 100.0)- 0.5;
w120 = ((rand() % 100) / 100.0)- 0.5;
w210 = ((rand() % 100) / 100.0)- 0.5;
w220 = ((rand() % 100) / 100.0)- 0.5;
u110 = ((rand() % 100) / 100.0) - 0.5;
u120 = ((rand() % 100) / 100.0) - 0.5;
u210 = ((rand() % 100) / 100.0) - 0.5;
u220 = ((rand() % 100) / 100.0) - 0.5;
fprintf(net, "%g %g %g %g %g %g %g %g %g %g %g %g 1.0 1.0\n", b10, b20, b30, b40, u110, u120, u210, u220, w110, w120, w210, w220);
}
void openFiles() {
net = fopen("net.txt", "w");
testExamples = fopen("testExamples.txt", "w");
import = fopen(importFile, "r");
if (net == NULL || testExamples == NULL || import == NULL) { printf("Error opening file!\n"); }
}
void importNN() {
if(IMPORT) {
char labelsANN[12][25];
int c = 0; int i = 0; int counter = 0;
while ( (c = fgetc (import)) != EOF ){
if ( c == ' ' ){ counter++; i = 0; continue; }
labelsANN[counter][i] = c;
i++;
}
b10 = atof(labelsANN[0]);
b20 = atof(labelsANN[1]);
b30 = atof(labelsANN[2]);
b40 = atof(labelsANN[3]);
w110 = atof(labelsANN[4]);
w120 = atof(labelsANN[5]);
w210 = atof(labelsANN[6]);
w220 = atof(labelsANN[7]);
u110 = atof(labelsANN[8]);
u120 = atof(labelsANN[9]);
u210 = atof(labelsANN[10]);
u220 = atof(labelsANN[11]);
}
}
void train() {
double answer1, answer2;
for (size_t j = 0; j < NUMEPOCH; j++) {
for (size_t i = 0; i < EPOCH; i++) {
switch (ACTIVATION) {
case 0:
feedForrwardTanh(X1[i], X2[i], &answer1, &answer2);
backpropagationTanh(i);
break;
case 1:
feedForrwardReLU(X1[i], X2[i], &answer1, &answer2);
backpropagationReLU(i);
break;
case 2:
feedForrwardSigmoid(X1[i], X2[i], &answer1, &answer2);
backpropagationSigmoid(i);
break;
case 3:
feedForrwardDoubleSigmoid(X1[i], X2[i], &answer1, &answer2);
backpropagationDoubleSigmoid(i);
break;
}
fprintf(net, "%g %g %g %g %g %g %g %g %g %g %g %g %g %g\n", b10, b20, b30, b40, u11, u12, u21, u22, w110, w120, w210, w220, (z1 - T1[i]), (z2 - T2[i]));
}
}
}
void test() {
double ans1, ans2;
int errorCount = 0;
float totalError = 0.0;
for (size_t i = 0; i < EPOCH; i++) {
x1 = (rand() % 2); x2 = (rand() % 2);
int testT1 = (x1 == x2) ? 0 : 1;
int testT2 = (x1 && x2) ? 1 : 0;
switch (ACTIVATION) {
case 0:
feedForrwardTanh(x1, x2, &ans1, &ans2);
break;
case 1:
feedForrwardReLU(x1, x2, &ans1, &ans2);
break;
case 2:
feedForrwardSigmoid(x1, x2, &ans1, &ans2);
break;
case 3:
feedForrwardDoubleSigmoid(x1, x2, &ans1, &ans2);
break;
}
int answer1, answer2;
answer1 = z1 + ROUND;
answer2 = z2 + ROUND;
fprintf(testExamples, "%g %g %g %g %d %d %d %d\n", x1, x2, z1, z2, answer1, answer2, testT1, testT2);
if(answer1 != (testT1) || (answer2 != (testT2))) errorCount++;
totalError += 0.5 * (((z1 - testT1) * (z1 - testT1)) + ((z2 - testT2) * (z2 - testT2)));
}
printNN();
printf("ERROR AVERAGE: %g ERROR COUNT: %d\n", totalError/EPOCH, errorCount);
}
void exportNeuralNetwork() {
FILE * export = fopen("export.txt", "w");
fprintf(export, "%g %g %g %g %g %g %g %g %g %g %g %g\n", b10, b20, b30, b40, u11, u12, u21, u22, w110, w120, w210, w220);
fclose(export);
}
void createOutputGraph() {
FILE * graphX = fopen("docs/graph/100x100Xor.txt", "w");
FILE * graphA = fopen("docs/graph/100x100And.txt", "w");
double answer1, answer2;
for (size_t i = 0; i < 100; i++) {
for (size_t j = 0; j < 100; j++) {
switch (ACTIVATION) {
case 0:
feedForrwardTanh(i/100.0, j/100.0, &answer1, &answer2);
break;
case 1:
feedForrwardReLU(i/100.0, j/100.0, &answer1, &answer2);
break;
case 2:
feedForrwardSigmoid(i/100.0, j/100.0, &answer1, &answer2);
break;
case 3:
feedForrwardDoubleSigmoid(i/100.0, j/100.0, &answer1, &answer2);
break;
}
fprintf(graphX, "%g ", answer1);
fprintf(graphA, "%g ", answer2);
}
fprintf(graphX, "\n");
fprintf(graphA, "\n");
}
fclose(graphX);
fclose(graphA);
}
void userInput() {
int user1, user2;
if(INTERACTION) {
printf("Use the NN: \n");
int test = 1;
double answer1, answer2;
while(test) {
printf("Enter input 1: "); scanf("%d", &user1);
printf("Enter input 2: "); scanf("%d", &user2);
if(user1 < 0 || user1 > 1 || user2 < 0 || user2 > 1){ printf("INPUT ERROR\n"); break; };
printf("Running NN on (%d %d)\n", user1, user2);
switch (ACTIVATION) {
case 0:
feedForrwardTanh(user1, user2, &answer1, &answer2);
break;
case 1:
feedForrwardReLU(user1, user2, &answer1, &answer2);
break;
case 2:
feedForrwardSigmoid(user1, user2, &answer1, &answer2);
break;
case 3:
feedForrwardDoubleSigmoid(user1, user2, &answer1, &answer2);
break;
}
printf("Xor: %g\n", answer1);
printf("And: %g\n", answer2);
}
}
}
void func_closeFiles() {
fclose(net);
fclose(testExamples);
fclose(import);
}
void feedForrwardTanh(double _x1, double _x2, double * ans1, double * ans2) {
x1 = _x1; x2 = _x2;
s1 = w110 * x1 + w210 * x2 + b10;
s2 = w120 * x1 + w220 * x2 + b20;
y10 = tanh(s1);
y2 = tanh(s2);
s3 = u110 * y10 + u210 * y2 + b30;
s4 = u120 * y10 + u220 * y2 + b40;
y3 = tanh(s3);
y4 = tanh(s4);
z1 = y3;
z2 = y4;
*ans1 = z1;
*ans2 = z2;
}
void backpropagationTanh(int i) {
b4 = b40 - E * ((z2 - T2[i]) * (1.0 - z2 * z2));
b3 = b30 - E * ((z1 - T1[i]) * (1.0 - z1 * z1));
u11 = u110 - E * ((z1 - T1[i]) * (1.0 - z1 * z1) * y10);
u12 = u120 - E * ((z2 - T2[i]) * (1.0 - z2 * z2) * y10);
u21 = u210 - E * ((z1 - T1[i]) * (1.0 - z1 * z1) * y2);
u22 = u220 - E * ((z2 - T2[i]) * (1.0 - z2 * z2) * y2);
b2 = b20 - E * ((((z1 - T1[i]) * (1.0 - z1 * z1) * u21) + ((z2 - T2[i]) * (1.0 - z2 * z2) * u22)) * (1.0 - y2 * y2));
b1 = b10 - E * ((((z1 - T1[i]) * (1.0 - z1 * z1) * u11) + ((z2 - T2[i]) * (1.0 - z2 * z2) * u12)) * (1.0 - y10 * y10));
w11 = w110 - E * ((((z1 - T1[i]) * (1.0 - z1 * z1) * u11) + ((z2 - T2[i]) * (1.0 - z2 * z2) * u12)) * (1.0 - y10 * y10) * x1);
w12 = w120 - E * ((((z1 - T1[i]) * (1.0 - z1 * z1) * u21) + ((z2 - T2[i]) * (1.0 - z2 * z2) * u22)) * (1.0 - y2 * y2) * x1);
w21 = w210 - E * ((((z1 - T1[i]) * (1.0 - z1 * z1) * u11) + ((z2 - T2[i]) * (1.0 - z2 * z2) * u12)) * (1.0 - y10 * y10) * x2);
w22 = w220 - E * ((((z1 - T1[i]) * (1.0 - z1 * z1) * u21) + ((z2 - T2[i]) * (1.0 - z2 * z2) * u22)) * (1.0 - y2 * y2) * x2);
backproagationhwsolution();
}
void feedForrwardReLU(double _x1, double _x2, double * ans1, double * ans2) {
x1 = _x1; x2 = _x2;
s1 = w110 * x1 + w210 * x2 + b10;
s2 = w120 * x1 + w220 * x2 + b20;
y10 = (s1 > 0) ? s1 : 0;
y2 = (s2 > 0) ? s2 : 0;
s3 = u110 * y10 + u210 * y2 + b30;
s4 = u120 * y10 + u220 * y2 + b40;
y3 = (s3 > 0) ? s3 : 0;
y4 = (s4 > 0) ? s4 : 0;
z1 = y3;
z2 = y4;
*ans1 = z1;
*ans2 = z2;
}
void backpropagationReLU(int i) {
int tempErr1, tempErr2, tempErr3, tempErr4;
tempErr1 = (s1 > 0) ? 1 : 0;
tempErr2 = (s2 > 0) ? 1 : 0;
tempErr3 = (s3 > 0) ? 1 : 0;
tempErr4 = (s4 > 0) ? 1 : 0;
b4 = (s4 > 0) ? b40 - E * (z2 - T2[i]) : b40;
b3 = (s3 > 0) ? b30 - E * (z1 - T1[i]) : b30;
u11 = (s3 > 0) ? u110 - (E * (z1 - T1[i]) * y10) : u110;
u12 = (s4 > 0) ? u120 - (E * (z2 - T2[i]) * y10) : u120;
u21 = (s3 > 0) ? u210 - (E * (z1 - T1[i]) * y2) : u210;
u22 = (s4 > 0) ? u220 - (E * (z2 - T2[i]) * y2) : u220;
b2 = b20 - E * (((z1 - T1[i]) * tempErr3 * u21 * tempErr2) + ((z2 - T2[i]) * tempErr4 * u22 * tempErr2));
b1 = b10 - E * (((z1 - T1[i]) * tempErr3 * u11 * tempErr1) + ((z2 - T2[i]) * tempErr4 * u12 * tempErr1));
w11 = w110 - E * (((z1 - T1[i]) * tempErr3 * u11 * tempErr1 * x1) + ((z2 - T2[i]) * tempErr4 * u12 * tempErr1 * x1));
w12 = w120 - E * (((z1 - T1[i]) * tempErr3 * u21 * tempErr2 * x1) + ((z2 - T2[i]) * tempErr4 * u22 * tempErr2 * x1));
w21 = w210 - E * (((z1 - T1[i]) * tempErr3 * u11 * tempErr1 * x2) + ((z2 - T2[i]) * tempErr4 * u12 * tempErr1 * x2));
w22 = w220 - E * (((z1 - T1[i]) * tempErr3 * u21 * tempErr2 * x2) + ((z2 - T2[i]) * tempErr4 * u22 * tempErr2 * x2));
backproagationhwsolution();
}
double sigmoid(double x) {
return (1 / (1 + exp(-1 * x)));
}
double doubleSigmoid(double x) {
return 2 * (sigmoid(x) - 0.5);
}
double sigmoidDeri(double x) {
return (exp(x) / pow((1 + exp(x)), 2));
}
double double_SigmoidDeri(double x) {
return sigmoidDeri(x);
}
void feedForrwardSigmoid(double _x1, double _x2, double *ans1, double *ans2) {
x1 = _x1; x2 = _x2;
s1 = w110 * x1 + w210 * x2 + b10;
s2 = w120 * x1 + w220 * x2 + b20;
y10 = sigmoid(s1);
y2 = sigmoid(s2);
s3 = u110 * y10 + u210 * y2 + b30;
s4 = u120 * y10 + u220 * y2 + b40;
y3 = sigmoid(s3);
y4 = sigmoid(s4);
z1 = y3;
z2 = y4;
*ans1 = z1;
*ans2 = z2;
}
void backpropagationSigmoid(int i) {
b4 = b40 - E * (z2 - T2[i]) * sigmoidDeri(s4);
b3 = b30 - E * (z1 - T1[i]) * sigmoidDeri(s3);
u11 = u110 - E * (z1 - T1[i]) * sigmoidDeri(s3) * y10;
u12 = u120 - E * (z2 - T2[i]) * sigmoidDeri(s4) * y10;
u21 = u210 - E * (z1 - T1[i]) * sigmoidDeri(s3) * y2;
u22 = u220 - E * (z2 - T2[i]) * sigmoidDeri(s4) * y2;
b2 = b20 - E * (((z1 - T1[i]) * sigmoidDeri(s3) * u21 * sigmoidDeri(s2)) + ((z2 - T2[i]) * sigmoidDeri(s4) * u22 * sigmoidDeri(s2)));
b1 = b10 - E * (((z1 - T1[i]) * sigmoidDeri(s3) * u11 * sigmoidDeri(s1)) + ((z2 - T2[i]) * sigmoidDeri(s4) * u12 * sigmoidDeri(s1)));
w11 = w110 - E * (((z1 - T1[i]) * sigmoidDeri(s3) * u11 * sigmoidDeri(s1) * x1) + ((z2 - T2[i]) * sigmoidDeri(s4) * u12 * sigmoidDeri(s1) * x1));
w12 = w120 - E * (((z1 - T1[i]) * sigmoidDeri(s3) * u21 * sigmoidDeri(s2) * x1) + ((z2 - T2[i]) * sigmoidDeri(s4) * u22 * sigmoidDeri(s2) * x1));
w21 = w210 - E * (((z1 - T1[i]) * sigmoidDeri(s3) * u11 * sigmoidDeri(s1) * x2) + ((z2 - T2[i]) * sigmoidDeri(s4) * u12 * sigmoidDeri(s1) * x2));
w22 = w220 - E * (((z1 - T1[i]) * sigmoidDeri(s3) * u21 * sigmoidDeri(s2) * x2) + ((z2 - T2[i]) * sigmoidDeri(s4) * u22 * sigmoidDeri(s2) * x2));
backproagationhwsolution();
}
void feedForrwardDoubleSigmoid(double _x1, double _x2, double *ans1, double *ans2) {
x1 = _x1; x2 = _x2;
s1 = w110 * x1 + w210 * x2 + b10;
s2 = w120 * x1 + w220 * x2 + b20;
y10 = doubleSigmoid(s1);
y2 = doubleSigmoid(s2);
s3 = u110 * y10 + u210 * y2 + b30;
s4 = u120 * y10 + u220 * y2 + b40;
y3 = doubleSigmoid(s3);
y4 = doubleSigmoid(s4);
z1 = y3;
z2 = y4;
*ans1 = z1;
*ans2 = z2;
}
void backpropagationDoubleSigmoid(int i) {
b4 = b40 - E * (z2 - T2[i]) * double_SigmoidDeri(s4);
b3 = b30 - E * (z1 - T1[i]) * double_SigmoidDeri(s3);
u11 = u110 - E * (z1 - T1[i]) * double_SigmoidDeri(s3) * y10;
u12 = u120 - E * (z2 - T2[i]) * double_SigmoidDeri(s4) * y10;
u21 = u210 - E * (z1 - T1[i]) * double_SigmoidDeri(s3) * y2;
u22 = u220 - E * (z2 - T2[i]) * double_SigmoidDeri(s4) * y2;
b2 = b20 - E * (((z1 - T1[i]) * double_SigmoidDeri(s3) * u21 * double_SigmoidDeri(s2)) + ((z2 - T2[i]) * double_SigmoidDeri(s4) * u22 * double_SigmoidDeri(s2)));
b1 = b10 - E * (((z1 - T1[i]) * double_SigmoidDeri(s3) * u11 * double_SigmoidDeri(s1)) + ((z2 - T2[i]) * double_SigmoidDeri(s4) * u12 * double_SigmoidDeri(s1)));
w11 = w110 - E * (((z1 - T1[i]) * double_SigmoidDeri(s3) * u11 * double_SigmoidDeri(s1) * x1) + ((z2 - T2[i]) * double_SigmoidDeri(s4) * u12 * double_SigmoidDeri(s1) * x1));
w12 = w120 - E * (((z1 - T1[i]) * double_SigmoidDeri(s3) * u21 * double_SigmoidDeri(s2) * x1) + ((z2 - T2[i]) * double_SigmoidDeri(s4) * u22 * double_SigmoidDeri(s2) * x1));
w21 = w210 - E * (((z1 - T1[i]) * double_SigmoidDeri(s3) * u11 * double_SigmoidDeri(s1) * x2) + ((z2 - T2[i]) * double_SigmoidDeri(s4) * u12 * double_SigmoidDeri(s1) * x2));
w22 = w220 - E * (((z1 - T1[i]) * double_SigmoidDeri(s3) * u21 * double_SigmoidDeri(s2) * x2) + ((z2 - T2[i]) * double_SigmoidDeri(s4) * u22 * double_SigmoidDeri(s2) * x2));
backproagationhwsolution();
}
void backproagationhwsolution() {
b40 = b4;
b30 = b3;
u110 = u11;
u120 = u12;
u210 = u21;
u220 = u22;
b10 = b1;
b20 = b2;
w110 = w11;
w210 = w21;
w120 = w12;
w220 = w22;
}
void printNN() {
printf("\n");
printf("b10: %g\n", b10);
printf("b20: %g\n", b20);
printf("b30: %g\n", b30);
printf("b40: %g\n", b40);
printf("\n");
printf("u110: %g\n", u110);
printf("u120: %g\n", u120);
printf("u210: %g\n", u210);
printf("u220: %g\n", u220);
printf("\n");
printf("w110: %g\n", w110);
printf("w120: %g\n", w120);
printf("w210: %g\n", w210);
printf("w220: %g\n", w220);
printf("\n");
}
New folder (2)/export.txt
-0.563546 -2.08333 -0.697688 1.20124 1.77022 -0.41627 -1.6531 1.45947 1.79344 1.42269 1.81199 1.42123
New folder (2)/import.txt
-0.299554 2.14398 -1.19435 1.54533 2.3576 -0.491017 1.93363 -1.73746 2.2662 -1.42269 2.05547 -1.41482
New folder (2)/net.txt
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