void main()
{
int i, j, k,
trialnr;
/* input pars */
getpars();
/* input training set and test set */
getsets();
if (maxtrials>20)
maxtrials=20;
if (bias1==1)
in_mod++;
if (bias2==1)
hid_mod++;
hi_in_mod = in_mod+hid_mod;
cell_mod=hi_in_mod;
for (i=0;i<num_blocks;i++)
cell_mod+=(2+block_size[i]);
ges_mod = cell_mod+out_mod;
if (ges_mod>max_units)
{
printf("Program terminated!\n");
printf("You have to set the constant max_units at begin\n");
printf("of the program file greater or equal %d and then\n",ges_mod);
printf("compile the program again.\n");
exit(0);
}
srand(ran_sta);
for (trialnr=0;trialnr<maxtrials;trialnr++)
{
outfile = outf[trialnr];
weightfile = weig[trialnr];
fp1 = fopen(outfile, "w");
fprintf(fp1,"Trial Nr.:%.1d\n",trialnr);
fclose(fp1);
fp2 = fopen(weightfile, "w");
fprintf(fp2,"Trial Nr.:%.1d\n",trialnr);
fclose(fp2);
initia();
examples=0;
epoch=0;
maxepoch=maxepoch_init;
stop_learn=0;
learn = 1;
while (learn == 1)
{
/* executing the environment
and setting the input
*/
execute_act();
/* forward pass */
forward_pass();
if (targ==1) /* only if target for this input */
{
/* compute error */
for (k=cell_mod,j=0;k<ges_mod;k++,j++)
{
error[j]= target_a[j] - Yk_mod_new[k];
};
/* Training error */
comp_err();
}
/* backward pass */
if (targ==1) /* only if target for this input */
{
backward_pass();
}
else
{
derivatives();
}
/* set old activations */
for (i=0;i<ges_mod;i++)
{
Yk_mod_old[i] = Yk_mod_new[i];
}
/* update weights */
if (weight_up==1)
{
weight_up=0;
weight_update();
}
/* stop if maxepoch reached */
if (epoch>maxepoch)
learn=0;
}
weight_out();
test();
}
exit(0);
}
int main(){
int a,b;
char c;
int i;
int j;
double dif;
int col = 0;
double sum_sq_error = 0 ;
int epoch=0;
double pre_avg_sq_error = 0;
double accuracy = 0;
double max = 0;
int num_correct= 0, tmp, k;
//�ʱ�ȭ
for(i = 0; i<NLayer; i++){
for(j = 0; j<MLayerSize; j++){
s[i][j] = 0;
f[i][j] = 0;
delta[i][j] = 0;
}
}
initialization(); // ����ġ ������ �ʱ�ȭ
FILE *fp, *fp1;
fp = fopen("traindata.txt", "r");fp1 = fopen("testdata.txt", "r");//�Է½�ȣ ��������
while(!feof(fp)){
fscanf(fp, "%d", &a);
for(i = 0; i<m2; i++){
if(a == i)
d[col][i] = 1;
else
d[col][i] = 0;
}
fscanf(fp, " %c ", &c); c = '0'; // $������
for(i = 0; i<N; i++){
fscanf(fp, "%d", &b);
TrainData[col][i] = b;
}
col ++;
}
col = 0;a = 0;b = 0;
while(!feof(fp1)) {
fscanf(fp1, "%d", &a);
for(i=0; i<m2; i++) {
if(a==i)
Td[col][i]=1;
else
Td[col][i]=0;
}
fscanf(fp1, " %c ", &c); c = '0'; // $������
for(i=0; i<N; i++) {
fscanf(fp1, " %d", &b);
TestData[col][i]=b;
}
col++;
}
//file input
do{
pre_avg_sq_error = avg_sq_error;
// printf("pre = %lf\n", pre_avg_sq_error);
avg_sq_error=0, sum_sq_error=0;
for(int t=0; t<=N_tr_examples-1; t++){ // �� ������ �Ʒð���
forward_compute(t);
backward_compute(t);
weight_update(t);
}
epoch++;
for(int t = 0; t<=N_tr_examples-1; t++){ // �� ���� �� ��� ���� ���
// t���� �Ʒÿ����� �̿��Ͽ� �ý����� ����� ���� error�� ����
forward_compute(t); // t ��° �Ʒÿ��� �����Ͽ� ����� ����
for(i = 0; i<=M[NLayer-1]-1 ; i++){ // �������� ����� �̿��Ͽ� ������ ����.
sum_sq_error+=pow((d[t][i]-f[NLayer-1][i]),2);
}
}
avg_sq_error = sum_sq_error/(N_tr_examples*M[NLayer-1]); //��� �Ʒ� ���� ���Ͽ� ��� square ������ �����
printf("%d epoch error rate = %lf\n",epoch,avg_sq_error);
/* Fail �κ�, �Ӱ�ġ ���Ϸ� �������� �ʹ� ���� ����� ������ ������ �Ͼ���ϴ�.
dif = pre_avg_sq_error - avg_sq_error;
if(dif < 0)
dif = -dif;
printf("dif = %lf\n", dif);
*/
}while(threshold < avg_sq_error) ;
//&& (threshold < dif)); //avg_sq_error �� �Ӱ�ġ �����̸� ����,
//�Ǵ� ���� epoch ������ avg_sq_error �� �̹� epoch �� avg_sq_error �� ���̰� �Ӱ�ġ �����̸� ����
for(int t = 0; t<=N_te_examples-1; t++){
// TestData[t], d_te[t] �� input,d�� �ִ´�.
t_forward_compute(t);
max=0.;
for(j=0; j<NLayer; j++) {
if(max < f[NLayer-1][j]) {
max = f[NLayer-1][j];
tmp = j;
}
}
for(k=0; k<NLayer; k++) {
if(Td[t][k] == 1 && tmp == k)
num_correct++;
}
}
accuracy = (double)num_correct/(double)N_te_examples;
printf("Accuracy = %lf", accuracy);
fclose(fp);
fclose(fp1);
}
