#include <stdio.h>
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#include "network.h"
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#include "image.h"
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#include "data.h"
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#include "connected_layer.h"
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#include "convolutional_layer.h"
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#include "maxpool_layer.h"
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network make_network(int n)
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{
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network net;
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net.n = n;
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net.layers = calloc(net.n, sizeof(void *));
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net.types = calloc(net.n, sizeof(LAYER_TYPE));
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return net;
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}
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void forward_network(network net, double *input)
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{
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int i;
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for(i = 0; i < net.n; ++i){
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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forward_convolutional_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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forward_connected_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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forward_maxpool_layer(layer, input);
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input = layer.output;
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}
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}
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}
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void update_network(network net, double step)
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{
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int i;
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for(i = 0; i < net.n; ++i){
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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update_convolutional_layer(layer, step);
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}
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else if(net.types[i] == MAXPOOL){
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//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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update_connected_layer(layer, step, .3, 0);
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}
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}
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}
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double *get_network_output_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.output;
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}
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return 0;
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}
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double *get_network_output(network net)
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{
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return get_network_output_layer(net, net.n-1);
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}
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double *get_network_delta_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return layer.delta;
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} else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return layer.delta;
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} else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.delta;
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}
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return 0;
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}
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double *get_network_delta(network net)
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{
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return get_network_delta_layer(net, net.n-1);
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}
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void learn_network(network net, double *input)
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{
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int i;
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double *prev_input;
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double *prev_delta;
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for(i = net.n-1; i >= 0; --i){
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if(i == 0){
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prev_input = input;
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prev_delta = 0;
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}else{
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prev_input = get_network_output_layer(net, i-1);
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prev_delta = get_network_delta_layer(net, i-1);
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}
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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learn_convolutional_layer(layer, prev_input);
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if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
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}
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else if(net.types[i] == MAXPOOL){
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//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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learn_connected_layer(layer, prev_input);
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if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
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}
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}
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}
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void train_network_batch(network net, batch b)
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{
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int i,j;
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int k = get_network_output_size(net);
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int correct = 0;
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for(i = 0; i < b.n; ++i){
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forward_network(net, b.images[i].data);
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image o = get_network_image(net);
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double *output = get_network_output(net);
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double *delta = get_network_delta(net);
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for(j = 0; j < k; ++j){
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//printf("%f %f\n", b.truth[i][j], output[j]);
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delta[j] = b.truth[i][j]-output[j];
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if(fabs(delta[j]) < .5) ++correct;
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//printf("%f\n", output[j]);
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}
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learn_network(net, b.images[i].data);
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update_network(net, .00001);
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}
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printf("Accuracy: %f\n", (double)correct/b.n);
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}
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int get_network_output_size_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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image output = get_convolutional_image(layer);
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return output.h*output.w*output.c;
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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image output = get_maxpool_image(layer);
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return output.h*output.w*output.c;
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.outputs;
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}
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return 0;
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}
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int get_network_output_size(network net)
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{
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int i = net.n-1;
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return get_network_output_size_layer(net, i);
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}
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image get_network_image_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return get_convolutional_image(layer);
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return get_maxpool_image(layer);
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}
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return make_image(0,0,0);
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}
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image get_network_image(network net)
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{
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int i;
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for(i = net.n-1; i >= 0; --i){
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image m = get_network_image_layer(net, i);
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if(m.h != 0) return m;
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}
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return make_image(1,1,1);
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}
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void visualize_network(network net)
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{
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int i;
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for(i = 0; i < 1; ++i){
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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visualize_convolutional_layer(layer);
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}
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}
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}
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