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This commit is contained in:
Joseph Redmon
2013-12-02 16:41:40 -08:00
parent 2db9fbef2b
commit 0d6bb5d44d
29 changed files with 836 additions and 214 deletions
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100
src/network.c
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@ -2,10 +2,12 @@
#include "network.h"
#include "image.h"
#include "data.h"
#include "utils.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
#include "softmax_layer.h"
network make_network(int n)
{
@ -30,6 +32,11 @@ void forward_network(network net, double *input)
forward_connected_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
forward_softmax_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
forward_maxpool_layer(layer, input);
@ -44,14 +51,17 @@ void update_network(network net, double step)
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
update_convolutional_layer(layer, step);
update_convolutional_layer(layer, step, 0.9, .01);
}
else if(net.types[i] == MAXPOOL){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == SOFTMAX){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer, step, .3, 0);
update_connected_layer(layer, step, .9, 0);
}
}
}
@ -64,6 +74,9 @@ double *get_network_output_layer(network net, int i)
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.output;
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.output;
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
@ -83,6 +96,9 @@ double *get_network_delta_layer(network net, int i)
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@ -114,7 +130,12 @@ void learn_network(network net, double *input)
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];
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
@ -130,19 +151,33 @@ void train_network_batch(network net, batch b)
int k = get_network_output_size(net);
int correct = 0;
for(i = 0; i < b.n; ++i){
show_image(b.images[i], "Input");
forward_network(net, b.images[i].data);
image o = get_network_image(net);
if(o.h) show_image_collapsed(o, "Output");
double *output = get_network_output(net);
double *delta = get_network_delta(net);
int max_k = 0;
double max = 0;
for(j = 0; j < k; ++j){
//printf("%f %f\n", b.truth[i][j], output[j]);
delta[j] = b.truth[i][j]-output[j];
if(fabs(delta[j]) < .5) ++correct;
//printf("%f\n", output[j]);
if(output[j] > max) {
max = output[j];
max_k = j;
}
}
if(b.truth[i][max_k]) ++correct;
printf("%f\n", (double)correct/(i+1));
learn_network(net, b.images[i].data);
update_network(net, .00001);
update_network(net, .001);
if(i%100 == 0){
visualize_network(net);
cvWaitKey(100);
}
}
visualize_network(net);
print_network(net);
cvWaitKey(100);
printf("Accuracy: %f\n", (double)correct/b.n);
}
@ -162,6 +197,10 @@ int get_network_output_size_layer(network net, int i)
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.outputs;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
}
return 0;
}
@ -181,7 +220,7 @@ image get_network_image_layer(network net, int i)
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return get_maxpool_image(layer);
}
return make_image(0,0,0);
return make_empty_image(0,0,0);
}
image get_network_image(network net)
@ -191,17 +230,56 @@ image get_network_image(network net)
image m = get_network_image_layer(net, i);
if(m.h != 0) return m;
}
return make_image(1,1,1);
return make_empty_image(0,0,0);
}
void visualize_network(network net)
{
int i;
for(i = 0; i < 1; ++i){
char buff[256];
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];
visualize_convolutional_layer(layer);
visualize_convolutional_filters(layer, buff);
}
}
}
void print_network(network net)
{
int i,j;
for(i = 0; i < net.n; ++i){
double *output;
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] == 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;
}
double mean = mean_array(output, n);
double vari = variance_array(output, n);
printf("Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
if(n > 100) n = 100;
for(j = 0; j < n; ++j) printf("%f, ", output[j]);
if(n == 100)printf(".....\n");
printf("\n");
}
}