mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
804 lines
25 KiB
C
804 lines
25 KiB
C
#include <stdio.h>
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#include <time.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 "utils.h"
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#include "crop_layer.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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#include "cost_layer.h"
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#include "normalization_layer.h"
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#include "freeweight_layer.h"
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#include "softmax_layer.h"
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#include "dropout_layer.h"
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network make_network(int n, int batch)
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{
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network net;
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net.n = n;
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net.batch = batch;
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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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net.outputs = 0;
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net.output = 0;
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#ifdef GPU
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net.input_cl = calloc(1, sizeof(cl_mem));
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net.truth_cl = calloc(1, sizeof(cl_mem));
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#endif
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return net;
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}
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#ifdef GPU
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void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
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{
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//printf("start\n");
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int i;
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for(i = 0; i < net.n; ++i){
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//clock_t time = clock();
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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_gpu(layer, input);
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input = layer.output_cl;
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}
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else if(net.types[i] == COST){
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cost_layer layer = *(cost_layer *)net.layers[i];
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forward_cost_layer_gpu(layer, input, truth);
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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_gpu(layer, input);
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input = layer.output_cl;
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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_gpu(layer, input);
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input = layer.output_cl;
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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forward_softmax_layer_gpu(layer, input);
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input = layer.output_cl;
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}
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//printf("%d %f\n", i, sec(clock()-time));
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/*
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else if(net.types[i] == CROP){
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crop_layer layer = *(crop_layer *)net.layers[i];
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forward_crop_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == NORMALIZATION){
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normalization_layer layer = *(normalization_layer *)net.layers[i];
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forward_normalization_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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}
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void backward_network_gpu(network net, cl_mem input)
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{
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int i;
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cl_mem prev_input;
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cl_mem 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_cl_layer(net, i-1);
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prev_delta = get_network_delta_cl_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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backward_convolutional_layer_gpu(layer, prev_delta);
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}
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else if(net.types[i] == COST){
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cost_layer layer = *(cost_layer *)net.layers[i];
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backward_cost_layer_gpu(layer, prev_input, prev_delta);
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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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backward_connected_layer_gpu(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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backward_maxpool_layer_gpu(layer, prev_delta);
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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backward_softmax_layer_gpu(layer, prev_delta);
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}
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}
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}
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void update_network_gpu(network net)
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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_gpu(layer);
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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_gpu(layer);
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}
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}
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}
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cl_mem get_network_output_cl_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_cl;
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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.output_cl;
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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 layer.output_cl;
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.output_cl;
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}
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return 0;
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}
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cl_mem get_network_delta_cl_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_cl;
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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.delta_cl;
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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 layer.delta_cl;
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.delta_cl;
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}
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return 0;
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}
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#endif
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void forward_network(network net, float *input, float *truth, int train)
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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] == CROP){
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crop_layer layer = *(crop_layer *)net.layers[i];
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forward_crop_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == COST){
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cost_layer layer = *(cost_layer *)net.layers[i];
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forward_cost_layer(layer, input, truth);
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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forward_softmax_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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else if(net.types[i] == NORMALIZATION){
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normalization_layer layer = *(normalization_layer *)net.layers[i];
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forward_normalization_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == DROPOUT){
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if(!train) continue;
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dropout_layer layer = *(dropout_layer *)net.layers[i];
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forward_dropout_layer(layer, input);
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}
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else if(net.types[i] == FREEWEIGHT){
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if(!train) continue;
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freeweight_layer layer = *(freeweight_layer *)net.layers[i];
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forward_freeweight_layer(layer, input);
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}
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}
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}
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void update_network(network net)
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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);
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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] == SOFTMAX){
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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] == NORMALIZATION){
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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);
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}
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}
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}
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float *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] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == DROPOUT){
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return get_network_output_layer(net, i-1);
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} else if(net.types[i] == FREEWEIGHT){
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return get_network_output_layer(net, i-1);
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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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} else if(net.types[i] == NORMALIZATION){
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normalization_layer layer = *(normalization_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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float *get_network_output(network net)
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{
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int i;
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for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
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return get_network_output_layer(net, i);
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}
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float *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] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.delta;
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} else if(net.types[i] == DROPOUT){
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return get_network_delta_layer(net, i-1);
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} else if(net.types[i] == FREEWEIGHT){
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return get_network_delta_layer(net, i-1);
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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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float get_network_cost(network net)
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{
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if(net.types[net.n-1] == COST){
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return ((cost_layer *)net.layers[net.n-1])->output[0];
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}
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return 0;
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}
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float *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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float calculate_error_network(network net, float *truth)
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{
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float sum = 0;
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float *delta = get_network_delta(net);
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float *out = get_network_output(net);
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int i;
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for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
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//if(i %get_network_output_size(net) == 0) printf("\n");
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//printf("%5.2f %5.2f, ", out[i], truth[i]);
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//if(i == get_network_output_size(net)) printf("\n");
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delta[i] = truth[i] - out[i];
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//printf("%.10f, ", out[i]);
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sum += delta[i]*delta[i];
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}
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//printf("\n");
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return sum;
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}
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int get_predicted_class_network(network net)
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{
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float *out = get_network_output(net);
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int k = get_network_output_size(net);
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return max_index(out, k);
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}
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void backward_network(network net, float *input)
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{
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int i;
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float *prev_input;
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float *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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backward_convolutional_layer(layer, 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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if(i != 0) backward_maxpool_layer(layer, prev_delta);
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}
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else if(net.types[i] == NORMALIZATION){
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normalization_layer layer = *(normalization_layer *)net.layers[i];
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if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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if(i != 0) backward_softmax_layer(layer, prev_delta);
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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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backward_connected_layer(layer, prev_input, prev_delta);
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}
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else if(net.types[i] == COST){
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cost_layer layer = *(cost_layer *)net.layers[i];
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backward_cost_layer(layer, prev_input, prev_delta);
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}
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}
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}
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#ifdef GPU
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float train_network_datum_gpu(network net, float *x, float *y)
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{
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int x_size = get_network_input_size(net)*net.batch;
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int y_size = get_network_output_size(net)*net.batch;
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clock_t time = clock();
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if(!*net.input_cl){
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*net.input_cl = cl_make_array(x, x_size);
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*net.truth_cl = cl_make_array(y, y_size);
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}else{
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cl_write_array(*net.input_cl, x, x_size);
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cl_write_array(*net.truth_cl, y, y_size);
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}
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//printf("trans %f\n", sec(clock()-time));
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time = clock();
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forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
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//printf("forw %f\n", sec(clock()-time));
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time = clock();
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backward_network_gpu(net, *net.input_cl);
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//printf("back %f\n", sec(clock()-time));
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time = clock();
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float error = get_network_cost(net);
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update_network_gpu(net);
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//printf("updt %f\n", sec(clock()-time));
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time = clock();
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return error;
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}
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float train_network_sgd_gpu(network net, data d, int n)
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{
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int batch = net.batch;
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float *X = calloc(batch*d.X.cols, sizeof(float));
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float *y = calloc(batch*d.y.cols, sizeof(float));
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i){
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get_random_batch(d, batch, X, y);
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float err = train_network_datum_gpu(net, X, y);
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sum += err;
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}
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free(X);
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free(y);
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return (float)sum/(n*batch);
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}
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float train_network_data_gpu(network net, data d, int n)
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{
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int batch = net.batch;
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float *X = calloc(batch*d.X.cols, sizeof(float));
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float *y = calloc(batch*d.y.cols, sizeof(float));
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i){
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get_next_batch(d, batch, i*batch, X, y);
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float err = train_network_datum_gpu(net, X, y);
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sum += err;
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}
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free(X);
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free(y);
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return (float)sum/(n*batch);
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}
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#endif
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float train_network_datum(network net, float *x, float *y)
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{
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forward_network(net, x, y, 1);
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//int class = get_predicted_class_network(net);
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backward_network(net, x);
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float error = get_network_cost(net);
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update_network(net);
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//return (y[class]?1:0);
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return error;
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}
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float train_network_sgd(network net, data d, int n)
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{
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int batch = net.batch;
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float *X = calloc(batch*d.X.cols, sizeof(float));
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float *y = calloc(batch*d.y.cols, sizeof(float));
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i){
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get_random_batch(d, batch, X, y);
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float err = train_network_datum(net, X, y);
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sum += err;
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}
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free(X);
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free(y);
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return (float)sum/(n*batch);
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}
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float train_network_batch(network net, data d, int n)
|
|
{
|
|
int i,j;
|
|
float sum = 0;
|
|
int batch = 2;
|
|
for(i = 0; i < n; ++i){
|
|
for(j = 0; j < batch; ++j){
|
|
int index = rand()%d.X.rows;
|
|
float *x = d.X.vals[index];
|
|
float *y = d.y.vals[index];
|
|
forward_network(net, x, y, 1);
|
|
backward_network(net, x);
|
|
sum += get_network_cost(net);
|
|
}
|
|
update_network(net);
|
|
}
|
|
return (float)sum/(n*batch);
|
|
}
|
|
|
|
|
|
void train_network(network net, data d)
|
|
{
|
|
int i;
|
|
int correct = 0;
|
|
for(i = 0; i < d.X.rows; ++i){
|
|
correct += train_network_datum(net, d.X.vals[i], d.y.vals[i]);
|
|
if(i%100 == 0){
|
|
visualize_network(net);
|
|
cvWaitKey(10);
|
|
}
|
|
}
|
|
visualize_network(net);
|
|
cvWaitKey(100);
|
|
fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
|
|
}
|
|
|
|
int get_network_input_size_layer(network net, int i)
|
|
{
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
return layer.h*layer.w*layer.c;
|
|
}
|
|
else if(net.types[i] == MAXPOOL){
|
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
|
return layer.h*layer.w*layer.c;
|
|
}
|
|
else if(net.types[i] == CONNECTED){
|
|
connected_layer layer = *(connected_layer *)net.layers[i];
|
|
return layer.inputs;
|
|
} else if(net.types[i] == DROPOUT){
|
|
dropout_layer layer = *(dropout_layer *) net.layers[i];
|
|
return layer.inputs;
|
|
}
|
|
else if(net.types[i] == FREEWEIGHT){
|
|
freeweight_layer layer = *(freeweight_layer *) net.layers[i];
|
|
return layer.inputs;
|
|
}
|
|
else if(net.types[i] == SOFTMAX){
|
|
softmax_layer layer = *(softmax_layer *)net.layers[i];
|
|
return layer.inputs;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
int get_network_output_size_layer(network net, int i)
|
|
{
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
image output = get_convolutional_image(layer);
|
|
return output.h*output.w*output.c;
|
|
}
|
|
else if(net.types[i] == MAXPOOL){
|
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
|
image output = get_maxpool_image(layer);
|
|
return output.h*output.w*output.c;
|
|
}
|
|
else if(net.types[i] == CONNECTED){
|
|
connected_layer layer = *(connected_layer *)net.layers[i];
|
|
return layer.outputs;
|
|
}
|
|
else if(net.types[i] == DROPOUT){
|
|
dropout_layer layer = *(dropout_layer *) net.layers[i];
|
|
return layer.inputs;
|
|
}
|
|
else if(net.types[i] == FREEWEIGHT){
|
|
freeweight_layer layer = *(freeweight_layer *) net.layers[i];
|
|
return layer.inputs;
|
|
}
|
|
else if(net.types[i] == SOFTMAX){
|
|
softmax_layer layer = *(softmax_layer *)net.layers[i];
|
|
return layer.inputs;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
int resize_network(network net, int h, int w, int c)
|
|
{
|
|
int i;
|
|
for (i = 0; i < net.n; ++i){
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer *layer = (convolutional_layer *)net.layers[i];
|
|
resize_convolutional_layer(layer, h, w, c);
|
|
image output = get_convolutional_image(*layer);
|
|
h = output.h;
|
|
w = output.w;
|
|
c = output.c;
|
|
}else if(net.types[i] == MAXPOOL){
|
|
maxpool_layer *layer = (maxpool_layer *)net.layers[i];
|
|
resize_maxpool_layer(layer, h, w, c);
|
|
image output = get_maxpool_image(*layer);
|
|
h = output.h;
|
|
w = output.w;
|
|
c = output.c;
|
|
}else if(net.types[i] == NORMALIZATION){
|
|
normalization_layer *layer = (normalization_layer *)net.layers[i];
|
|
resize_normalization_layer(layer, h, w, c);
|
|
image output = get_normalization_image(*layer);
|
|
h = output.h;
|
|
w = output.w;
|
|
c = output.c;
|
|
}else{
|
|
error("Cannot resize this type of layer");
|
|
}
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
int get_network_output_size(network net)
|
|
{
|
|
int i;
|
|
for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
|
|
return get_network_output_size_layer(net, i);
|
|
}
|
|
|
|
int get_network_input_size(network net)
|
|
{
|
|
return get_network_input_size_layer(net, 0);
|
|
}
|
|
|
|
image get_network_image_layer(network net, int i)
|
|
{
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
return get_convolutional_image(layer);
|
|
}
|
|
else if(net.types[i] == MAXPOOL){
|
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
|
return get_maxpool_image(layer);
|
|
}
|
|
else if(net.types[i] == NORMALIZATION){
|
|
normalization_layer layer = *(normalization_layer *)net.layers[i];
|
|
return get_normalization_image(layer);
|
|
}
|
|
else if(net.types[i] == CROP){
|
|
crop_layer layer = *(crop_layer *)net.layers[i];
|
|
return get_crop_image(layer);
|
|
}
|
|
return make_empty_image(0,0,0);
|
|
}
|
|
|
|
image get_network_image(network net)
|
|
{
|
|
int i;
|
|
for(i = net.n-1; i >= 0; --i){
|
|
image m = get_network_image_layer(net, i);
|
|
if(m.h != 0) return m;
|
|
}
|
|
return make_empty_image(0,0,0);
|
|
}
|
|
|
|
void visualize_network(network net)
|
|
{
|
|
image *prev = 0;
|
|
int i;
|
|
char buff[256];
|
|
//show_image(get_network_image_layer(net, 0), "Crop");
|
|
for(i = 0; i < net.n; ++i){
|
|
sprintf(buff, "Layer %d", i);
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
prev = visualize_convolutional_layer(layer, buff, prev);
|
|
}
|
|
if(net.types[i] == NORMALIZATION){
|
|
normalization_layer layer = *(normalization_layer *)net.layers[i];
|
|
visualize_normalization_layer(layer, buff);
|
|
}
|
|
}
|
|
}
|
|
|
|
void top_predictions(network net, int n, int *index)
|
|
{
|
|
int i,j;
|
|
int k = get_network_output_size(net);
|
|
float *out = get_network_output(net);
|
|
float thresh = FLT_MAX;
|
|
for(i = 0; i < n; ++i){
|
|
float max = -FLT_MAX;
|
|
int max_i = -1;
|
|
for(j = 0; j < k; ++j){
|
|
float val = out[j];
|
|
if(val > max && val < thresh){
|
|
max = val;
|
|
max_i = j;
|
|
}
|
|
}
|
|
index[i] = max_i;
|
|
thresh = max;
|
|
}
|
|
}
|
|
|
|
float *network_predict(network net, float *input)
|
|
{
|
|
forward_network(net, input, 0, 0);
|
|
float *out = get_network_output(net);
|
|
return out;
|
|
}
|
|
|
|
matrix network_predict_data_multi(network net, data test, int n)
|
|
{
|
|
int i,j,b,m;
|
|
int k = get_network_output_size(net);
|
|
matrix pred = make_matrix(test.X.rows, k);
|
|
float *X = calloc(net.batch*test.X.rows, sizeof(float));
|
|
for(i = 0; i < test.X.rows; i += net.batch){
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
|
|
}
|
|
for(m = 0; m < n; ++m){
|
|
float *out = network_predict(net, X);
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
for(j = 0; j < k; ++j){
|
|
pred.vals[i+b][j] += out[j+b*k]/n;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
free(X);
|
|
return pred;
|
|
}
|
|
|
|
matrix network_predict_data(network net, data test)
|
|
{
|
|
int i,j,b;
|
|
int k = get_network_output_size(net);
|
|
matrix pred = make_matrix(test.X.rows, k);
|
|
float *X = calloc(net.batch*test.X.rows, sizeof(float));
|
|
for(i = 0; i < test.X.rows; i += net.batch){
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
|
|
}
|
|
float *out = network_predict(net, X);
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
for(j = 0; j < k; ++j){
|
|
pred.vals[i+b][j] = out[j+b*k];
|
|
}
|
|
}
|
|
}
|
|
free(X);
|
|
return pred;
|
|
}
|
|
|
|
void print_network(network net)
|
|
{
|
|
int i,j;
|
|
for(i = 0; i < net.n; ++i){
|
|
float *output = 0;
|
|
int n = 0;
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
output = layer.output;
|
|
image m = get_convolutional_image(layer);
|
|
n = m.h*m.w*m.c;
|
|
}
|
|
else if(net.types[i] == MAXPOOL){
|
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
|
output = layer.output;
|
|
image m = get_maxpool_image(layer);
|
|
n = m.h*m.w*m.c;
|
|
}
|
|
else if(net.types[i] == CROP){
|
|
crop_layer layer = *(crop_layer *)net.layers[i];
|
|
output = layer.output;
|
|
image m = get_crop_image(layer);
|
|
n = m.h*m.w*m.c;
|
|
}
|
|
else if(net.types[i] == CONNECTED){
|
|
connected_layer layer = *(connected_layer *)net.layers[i];
|
|
output = layer.output;
|
|
n = layer.outputs;
|
|
}
|
|
else if(net.types[i] == SOFTMAX){
|
|
softmax_layer layer = *(softmax_layer *)net.layers[i];
|
|
output = layer.output;
|
|
n = layer.inputs;
|
|
}
|
|
float mean = mean_array(output, n);
|
|
float vari = variance_array(output, n);
|
|
fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
|
|
if(n > 100) n = 100;
|
|
for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
|
|
if(n == 100)fprintf(stderr,".....\n");
|
|
fprintf(stderr, "\n");
|
|
}
|
|
}
|
|
|
|
float network_accuracy(network net, data d)
|
|
{
|
|
matrix guess = network_predict_data(net, d);
|
|
float acc = matrix_accuracy(d.y, guess);
|
|
free_matrix(guess);
|
|
return acc;
|
|
}
|
|
|
|
float network_accuracy_multi(network net, data d, int n)
|
|
{
|
|
matrix guess = network_predict_data_multi(net, d, n);
|
|
float acc = matrix_accuracy(d.y, guess);
|
|
free_matrix(guess);
|
|
return acc;
|
|
}
|
|
|
|
|