int main() { Tensor * X_train = load_X("../data/train-images.idx3-ubyte", TRAIN_SIZE); float ** Y_train = load_Y("../data/train-labels.idx1-ubyte", TRAIN_SIZE); Tensor * X_test = load_X("../data/t10k-images.idx3-ubyte", TEST_SIZE); float ** Y_test = load_Y("../data/t10k-labels.idx1-ubyte", TEST_SIZE); const int num_layers = 2; Layer ** layers = new Layer*[num_layers]; layers[0] = new FullyConnectedLayer(100, 784, SIGMOID); layers[1] = new FullyConnectedLayer(10, 100, SIGMOID); // Train neural network ConvNet net = ConvNet(layers, num_layers, X_train, Y_train); net.train(0.01, 100, TRAIN_SIZE / 10, 10, TRAIN_SIZE); }
static int pvalues(int ii) { int i; char x[LLENGTH]; double *freq; long l; double a; strcpy(x,spb[ii]); i=load_X(x); if (i<0) return(-1); /* Rprintf("\ndim=%d,%d",mX,nX); getch(); */ i=data_open(word[1],&d); if (i<0) return(-1); v=(int *)muste_malloc(mX*sizeof(int)); if (v==NULL) { ei_tilaa(); return(-1); } i=nrot(); mT=mX; nT=2; rlabT=rlabX ; i=mat_alloc_lab(&T,mT,nT,NULL,&clabT); freq=T+mT; for (i=0; i<mX; ++i) freq[i]=0.0; i=conditions(&d); if (i<0) return(-1); n=0L; sur_print("\n"); for (l=d.l1; l<=d.l2; ++l) { if (unsuitable(&d)) continue; sprintf(sbuf,"%ld ",l); sur_print(sbuf); ++n; for (i=0; i<mX; ++i) { data_load(&d,l,v[i],&a); if (a==MISSING8) continue; if (a>X[i]) ++freq[i]; } } if (n==0L) { sur_print("\nNo active observations!"); WAIT; return(-1); } a=1.0/(double)n; for (i=0; i<mX; ++i) { T[i]=X[i]; freq[i]*=a; } strncpy(clabT,"Value P ",16); sprintf(exprT,"Tail_frequencies_in_data_%s_N=%d",word[1],n); save_T("TAILFREQ.M"); return(1); }