#include "connected_layer.h"
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//#include "old_conv.h"
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#include "convolutional_layer.h"
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#include "maxpool_layer.h"
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#include "network.h"
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#include "image.h"
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#include "parser.h"
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#include "data.h"
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#include "matrix.h"
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#include "utils.h"
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#include "mini_blas.h"
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#include <time.h>
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#include <stdlib.h>
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#include <stdio.h>
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#define _GNU_SOURCE
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#include <fenv.h>
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void test_convolve()
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{
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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image kernel = make_random_image(3,3,dog.c);
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image edge = make_image(dog.h, dog.w, 1);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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convolve(dog, kernel, 1, 0, edge, 1);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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}
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void test_convolve_matrix()
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{
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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int size = 11;
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int stride = 4;
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int n = 40;
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float *filters = make_random_image(size, size, dog.c*n).data;
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int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
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int mh = (size*size*dog.c);
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float *matrix = calloc(mh*mw, sizeof(float));
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image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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im2col_cpu(dog.data, dog.c, dog.h, dog.w, size, stride, matrix);
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gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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cvWaitKey(0);
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}
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void test_color()
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{
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image dog = load_image("test_color.png", 300, 400);
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show_image_layers(dog, "Test Color");
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}
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void verify_convolutional_layer()
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{
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srand(0);
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int i;
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int n = 1;
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int stride = 1;
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int size = 3;
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float eps = .00000001;
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image test = make_random_image(5,5, 1);
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convolutional_layer layer = *make_convolutional_layer(test.h,test.w,test.c, n, size, stride, RELU);
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image out = get_convolutional_image(layer);
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float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
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forward_convolutional_layer(layer, test.data);
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image base = copy_image(out);
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for(i = 0; i < test.h*test.w*test.c; ++i){
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test.data[i] += eps;
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forward_convolutional_layer(layer, test.data);
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image partial = copy_image(out);
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subtract_image(partial, base);
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scale_image(partial, 1/eps);
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jacobian[i] = partial.data;
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test.data[i] -= eps;
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}
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float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
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image in_delta = make_image(test.h, test.w, test.c);
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image out_delta = get_convolutional_delta(layer);
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for(i = 0; i < out.h*out.w*out.c; ++i){
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out_delta.data[i] = 1;
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backward_convolutional_layer(layer, in_delta.data);
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image partial = copy_image(in_delta);
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jacobian2[i] = partial.data;
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out_delta.data[i] = 0;
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}
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int j;
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float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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for(i = 0; i < test.h*test.w*test.c; ++i){
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for(j =0 ; j < out.h*out.w*out.c; ++j){
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j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
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j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
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printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
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}
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}
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image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
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image mj2 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
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printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
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show_image(mj1, "forward jacobian");
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show_image(mj2, "backward jacobian");
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}
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void test_load()
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{
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image dog = load_image("dog.jpg", 300, 400);
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show_image(dog, "Test Load");
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show_image_layers(dog, "Test Load");
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}
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void test_upsample()
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{
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image dog = load_image("dog.jpg", 300, 400);
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int n = 3;
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image up = make_image(n*dog.h, n*dog.w, dog.c);
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upsample_image(dog, n, up);
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show_image(up, "Test Upsample");
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show_image_layers(up, "Test Upsample");
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}
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void test_rotate()
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{
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int i;
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image dog = load_image("dog.jpg",300,400);
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clock_t start = clock(), end;
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for(i = 0; i < 1001; ++i){
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rotate_image(dog);
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}
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end = clock();
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printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image(dog, "Test Rotate");
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image random = make_random_image(3,3,3);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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}
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void test_parser()
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{
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network net = parse_network_cfg("test_parser.cfg");
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float input[1];
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int count = 0;
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float avgerr = 0;
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while(++count < 100000000){
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float v = ((float)rand()/RAND_MAX);
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float truth = v*v;
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input[0] = v;
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forward_network(net, input);
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float *out = get_network_output(net);
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float *delta = get_network_delta(net);
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float err = pow((out[0]-truth),2.);
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avgerr = .99 * avgerr + .01 * err;
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if(count % 1000000 == 0) printf("%f %f :%f AVG %f \n", truth, out[0], err, avgerr);
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delta[0] = truth - out[0];
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backward_network(net, input, &truth);
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update_network(net, .001,0,0);
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}
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}
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void test_data()
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{
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char *labels[] = {"cat","dog"};
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data train = load_data_image_pathfile_random("train_paths.txt", 101,labels, 2, 300, 400);
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free_data(train);
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}
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void test_full()
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{
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network net = parse_network_cfg("full.cfg");
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srand(2222222);
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int i = 800;
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char *labels[] = {"cat","dog"};
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float lr = .00001;
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float momentum = .9;
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float decay = 0.01;
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while(i++ < 1000 || 1){
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visualize_network(net);
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cvWaitKey(100);
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data train = load_data_image_pathfile_random("train_paths.txt", 1000, labels, 2, 256, 256);
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image im = float_to_image(256, 256, 3,train.X.vals[0]);
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show_image(im, "input");
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cvWaitKey(100);
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//scale_data_rows(train, 1./255.);
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normalize_data_rows(train);
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clock_t start = clock(), end;
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float loss = train_network_sgd(net, train, 100, lr, momentum, decay);
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end = clock();
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printf("%d: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
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free_data(train);
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if(i%100==0){
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char buff[256];
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sprintf(buff, "backup_%d.cfg", i);
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//save_network(net, buff);
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}
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//lr *= .99;
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}
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}
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void test_nist()
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{
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srand(444444);
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srand(888888);
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network net = parse_network_cfg("nist.cfg");
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data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
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data test = load_categorical_data_csv("mnist/mnist_test.csv",0,10);
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normalize_data_rows(train);
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normalize_data_rows(test);
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//randomize_data(train);
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int count = 0;
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float lr = .0005;
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float momentum = .9;
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float decay = 0.001;
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clock_t start = clock(), end;
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while(++count <= 100){
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//visualize_network(net);
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float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
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printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, loss, lr, momentum, decay);
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end = clock();
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printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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start=end;
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//cvWaitKey(100);
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//lr /= 2;
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if(count%5 == 0){
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float train_acc = network_accuracy(net, train);
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fprintf(stderr, "\nTRAIN: %f\n", train_acc);
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float test_acc = network_accuracy(net, test);
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fprintf(stderr, "TEST: %f\n\n", test_acc);
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printf("%d, %f, %f\n", count, train_acc, test_acc);
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//lr *= .5;
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}
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}
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}
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void test_ensemble()
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{
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int i;
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srand(888888);
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data d = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
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normalize_data_rows(d);
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data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
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normalize_data_rows(test);
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data train = d;
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// data *split = split_data(d, 1, 10);
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// data train = split[0];
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// data test = split[1];
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matrix prediction = make_matrix(test.y.rows, test.y.cols);
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int n = 30;
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for(i = 0; i < n; ++i){
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int count = 0;
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float lr = .0005;
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float momentum = .9;
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float decay = .01;
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network net = parse_network_cfg("nist.cfg");
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while(++count <= 15){
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float acc = train_network_sgd(net, train, train.X.rows, lr, momentum, decay);
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printf("Training Accuracy: %lf Learning Rate: %f Momentum: %f Decay: %f\n", acc, lr, momentum, decay );
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lr /= 2;
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}
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matrix partial = network_predict_data(net, test);
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float acc = matrix_accuracy(test.y, partial);
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printf("Model Accuracy: %lf\n", acc);
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matrix_add_matrix(partial, prediction);
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acc = matrix_accuracy(test.y, prediction);
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printf("Current Ensemble Accuracy: %lf\n", acc);
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free_matrix(partial);
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}
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float acc = matrix_accuracy(test.y, prediction);
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printf("Full Ensemble Accuracy: %lf\n", acc);
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}
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void test_random_classify()
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{
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network net = parse_network_cfg("connected.cfg");
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matrix m = csv_to_matrix("train.csv");
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//matrix ho = hold_out_matrix(&m, 2500);
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float *truth = pop_column(&m, 0);
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//float *ho_truth = pop_column(&ho, 0);
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int i;
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clock_t start = clock(), end;
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int count = 0;
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while(++count <= 300){
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for(i = 0; i < m.rows; ++i){
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int index = rand()%m.rows;
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//image p = float_to_image(1690,1,1,m.vals[index]);
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//normalize_image(p);
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forward_network(net, m.vals[index]);
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float *out = get_network_output(net);
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float *delta = get_network_delta(net);
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//printf("%f\n", out[0]);
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delta[0] = truth[index] - out[0];
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// printf("%f\n", delta[0]);
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//printf("%f %f\n", truth[index], out[0]);
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//backward_network(net, m.vals[index], );
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update_network(net, .00001, 0,0);
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}
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//float test_acc = error_network(net, m, truth);
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//float valid_acc = error_network(net, ho, ho_truth);
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//printf("%f, %f\n", test_acc, valid_acc);
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//fprintf(stderr, "%5d: %f Valid: %f\n",count, test_acc, valid_acc);
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//if(valid_acc > .70) break;
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}
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end = clock();
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FILE *fp = fopen("submission/out.txt", "w");
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matrix test = csv_to_matrix("test.csv");
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truth = pop_column(&test, 0);
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for(i = 0; i < test.rows; ++i){
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forward_network(net, test.vals[i]);
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float *out = get_network_output(net);
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if(fabs(out[0]) < .5) fprintf(fp, "0\n");
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else fprintf(fp, "1\n");
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}
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fclose(fp);
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printf("Neural Net Learning: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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}
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void test_split()
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{
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data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
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data *split = split_data(train, 0, 13);
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printf("%d, %d, %d\n", train.X.rows, split[0].X.rows, split[1].X.rows);
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}
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void test_im2row()
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{
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int h = 20;
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int w = 20;
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int c = 3;
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int stride = 1;
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int size = 11;
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image test = make_random_image(h,w,c);
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int mc = 1;
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int mw = ((h-size)/stride+1)*((w-size)/stride+1);
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int mh = (size*size*c);
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int msize = mc*mw*mh;
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float *matrix = calloc(msize, sizeof(float));
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int i;
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for(i = 0; i < 1000; ++i){
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im2col_cpu(test.data, c, h, w, size, stride, matrix);
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//image render = float_to_image(mh, mw, mc, matrix);
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}
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}
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void train_VOC()
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{
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network net = parse_network_cfg("cfg/voc_backup_ramp_80.cfg");
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srand(2222222);
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int i = 0;
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char *labels[] = {"aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"};
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float lr = .00001;
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float momentum = .9;
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float decay = 0.01;
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while(i++ < 1000 || 1){
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visualize_network(net);
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cvWaitKey(100);
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data train = load_data_image_pathfile_random("images/VOC2012/train_paths.txt", 1000, labels, 20, 300, 400);
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image im = float_to_image(300, 400, 3,train.X.vals[0]);
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show_image(im, "input");
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cvWaitKey(100);
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normalize_data_rows(train);
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clock_t start = clock(), end;
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float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
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end = clock();
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printf("%d: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
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free_data(train);
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if(i%10==0){
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char buff[256];
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sprintf(buff, "cfg/voc_backup_ramp_%d.cfg", i);
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save_network(net, buff);
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}
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//lr *= .99;
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}
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}
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int main()
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{
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//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
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//test_blas();
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//test_convolve_matrix();
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// test_im2row();
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//test_split();
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//test_ensemble();
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//test_nist();
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//test_full();
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train_VOC();
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//test_random_preprocess();
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//test_random_classify();
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//test_parser();
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//test_backpropagate();
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//test_ann();
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//test_convolve();
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//test_upsample();
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//test_rotate();
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//test_load();
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//test_network();
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//test_convolutional_layer();
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//verify_convolutional_layer();
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//test_color();
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//cvWaitKey(0);
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return 0;
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}
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