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About to do something stupid...

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This commit is contained in:
Joseph Redmon
2014-01-27 23:16:56 -08:00
parent ace5aeb0f5
commit b2b7137b6f
9 changed files with 218 additions and 173 deletions
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31
src/network.c
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@ -6,6 +6,7 @@
#include "connected_layer.h"
#include "convolutional_layer.h"
//#include "old_conv.h"
#include "maxpool_layer.h"
#include "softmax_layer.h"
@ -113,14 +114,17 @@ double *get_network_delta(network net)
return get_network_delta_layer(net, net.n-1);
}
void calculate_error_network(network net, double *truth)
double calculate_error_network(network net, double *truth)
{
double sum = 0;
double *delta = get_network_delta(net);
double *out = get_network_output(net);
int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){
delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
}
return sum;
}
int get_predicted_class_network(network net)
@ -130,9 +134,9 @@ int get_predicted_class_network(network net)
return max_index(out, k);
}
void backward_network(network net, double *input, double *truth)
double backward_network(network net, double *input, double *truth)
{
calculate_error_network(net, truth);
double error = calculate_error_network(net, truth);
int i;
double *prev_input;
double *prev_delta;
@ -146,8 +150,9 @@ void backward_network(network net, double *input, double *truth)
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
learn_convolutional_layer(layer, prev_input);
if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
learn_convolutional_layer(layer);
//learn_convolutional_layer(layer);
//if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@ -163,29 +168,31 @@ void backward_network(network net, double *input, double *truth)
if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
}
}
return error;
}
int train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
double train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
{
forward_network(net, x);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
double error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
return (y[class]?1:0);
//return (y[class]?1:0);
return error;
}
double train_network_sgd(network net, data d, int n, double step, double momentum,double decay)
{
int i;
int correct = 0;
double error = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
correct += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
//if((i+1)%10 == 0){
// printf("%d: %f\n", (i+1), (double)correct/(i+1));
//}
}
return (double)correct/n;
return error/n;
}
double train_network_batch(network net, data d, int n, double step, double momentum,double decay)
{
@ -282,7 +289,7 @@ void visualize_network(network net)
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
visualize_convolutional_filters(layer, buff);
visualize_convolutional_layer(layer, buff);
}
}
}