#include "blas.h"
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#include "math.h"
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#include <assert.h>
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#include <float.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
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{
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int b,i,j,k;
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int out_c = c/(stride*stride);
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for(b = 0; b < batch; ++b){
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for(k = 0; k < c; ++k){
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for(j = 0; j < h; ++j){
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for(i = 0; i < w; ++i){
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int in_index = i + w*(j + h*(k + c*b));
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int c2 = k % out_c;
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int offset = k / out_c;
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int w2 = i*stride + offset % stride;
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int h2 = j*stride + offset / stride;
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int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b));
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if(forward) out[out_index] = x[in_index];
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else out[in_index] = x[out_index];
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}
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}
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}
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}
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}
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void flatten(float *x, int size, int layers, int batch, int forward)
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{
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float *swap = calloc(size*layers*batch, sizeof(float));
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int i,c,b;
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for(b = 0; b < batch; ++b){
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for(c = 0; c < layers; ++c){
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for(i = 0; i < size; ++i){
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int i1 = b*layers*size + c*size + i;
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int i2 = b*layers*size + i*layers + c;
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if (forward) swap[i2] = x[i1];
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else swap[i1] = x[i2];
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}
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}
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}
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memcpy(x, swap, size*layers*batch*sizeof(float));
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free(swap);
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}
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void weighted_sum_cpu(float *a, float *b, float *s, int n, float *c)
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{
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int i;
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for(i = 0; i < n; ++i){
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c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0);
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}
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}
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void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out)
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{
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int stride = w1/w2;
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int sample = w2/w1;
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assert(stride == h1/h2);
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assert(sample == h2/h1);
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if(stride < 1) stride = 1;
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if(sample < 1) sample = 1;
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int minw = (w1 < w2) ? w1 : w2;
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int minh = (h1 < h2) ? h1 : h2;
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int minc = (c1 < c2) ? c1 : c2;
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int i,j,k,b;
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for(b = 0; b < batch; ++b){
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for(k = 0; k < minc; ++k){
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for(j = 0; j < minh; ++j){
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for(i = 0; i < minw; ++i){
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int out_index = i*sample + w2*(j*sample + h2*(k + c2*b));
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int add_index = i*stride + w1*(j*stride + h1*(k + c1*b));
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out[out_index] += add[add_index];
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}
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}
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}
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}
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}
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void mean_cpu(float *x, int batch, int filters, int spatial, float *mean)
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{
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float scale = 1./(batch * spatial);
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int i,j,k;
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for(i = 0; i < filters; ++i){
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mean[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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mean[i] += x[index];
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}
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}
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mean[i] *= scale;
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}
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}
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void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
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{
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float scale = 1./(batch * spatial - 1);
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int i,j,k;
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for(i = 0; i < filters; ++i){
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variance[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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variance[i] += pow((x[index] - mean[i]), 2);
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}
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}
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variance[i] *= scale;
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}
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}
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void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
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{
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int b, f, i;
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for(b = 0; b < batch; ++b){
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for(f = 0; f < filters; ++f){
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for(i = 0; i < spatial; ++i){
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int index = b*filters*spatial + f*spatial + i;
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x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f);
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}
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}
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}
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}
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void const_cpu(int N, float ALPHA, float *X, int INCX)
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{
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int i;
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for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
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}
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void mul_cpu(int N, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] *= X[i*INCX];
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}
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void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] = pow(X[i*INCX], ALPHA);
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}
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void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] += ALPHA*X[i*INCX];
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}
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void scal_cpu(int N, float ALPHA, float *X, int INCX)
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{
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int i;
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for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA;
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}
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void fill_cpu(int N, float ALPHA, float *X, int INCX)
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{
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int i;
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for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
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}
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void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX];
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}
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void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
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{
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int i;
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for(i = 0; i < n; ++i){
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float diff = truth[i] - pred[i];
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float abs_val = fabs(diff);
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if(abs_val < 1) {
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error[i] = diff * diff;
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delta[i] = diff;
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}
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else {
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error[i] = 2*abs_val - 1;
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delta[i] = (diff < 0) ? -1 : 1;
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}
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}
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}
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void l2_cpu(int n, float *pred, float *truth, float *delta, float *error)
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{
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int i;
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for(i = 0; i < n; ++i){
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float diff = truth[i] - pred[i];
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error[i] = diff * diff;
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delta[i] = diff;
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}
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}
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float dot_cpu(int N, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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float dot = 0;
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for(i = 0; i < N; ++i) dot += X[i*INCX] * Y[i*INCY];
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return dot;
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}
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void softmax(float *input, int n, float temp, float *output)
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{
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int i;
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float sum = 0;
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float largest = -FLT_MAX;
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for(i = 0; i < n; ++i){
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if(input[i] > largest) largest = input[i];
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}
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for(i = 0; i < n; ++i){
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float e = exp(input[i]/temp - largest/temp);
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sum += e;
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output[i] = e;
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}
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for(i = 0; i < n; ++i){
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output[i] /= sum;
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}
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}
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