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New data format

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This commit is contained in:
Joseph Redmon
2013-12-06 13:26:09 -08:00
parent b715671988
commit 4bdf96bd6a
12 changed files with 279 additions and 243 deletions
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97
src/network.c
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@ -15,6 +15,8 @@ network make_network(int n)
net.n = n;
net.layers = calloc(net.n, sizeof(void *));
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
net.output = 0;
return net;
}
@ -45,13 +47,13 @@ void forward_network(network net, double *input)
}
}
void update_network(network net, double step)
void update_network(network net, double step, double momentum, double decay)
{
int i;
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, 0.9, .01);
update_convolutional_layer(layer, step, momentum, decay);
}
else if(net.types[i] == MAXPOOL){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@ -61,7 +63,7 @@ void update_network(network net, double step)
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer, step, .9, 0);
update_connected_layer(layer, step, momentum, decay);
}
}
}
@ -111,8 +113,26 @@ double *get_network_delta(network net)
return get_network_delta_layer(net, net.n-1);
}
void learn_network(network net, double *input)
void calculate_error_network(network net, double *truth)
{
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];
}
}
int get_predicted_class_network(network net)
{
double *out = get_network_output(net);
int k = get_network_output_size(net);
return max_index(out, k);
}
void backward_network(network net, double *input, double *truth)
{
calculate_error_network(net, truth);
int i;
double *prev_input;
double *prev_delta;
@ -145,40 +165,43 @@ void learn_network(network net, double *input)
}
}
void train_network_batch(network net, batch b)
int train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
{
int i,j;
int k = get_network_output_size(net);
forward_network(net, x);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
update_network(net, step, momentum, decay);
return (y[class]?1:0);
}
double train_network_sgd(network net, data d, double step, double momentum,double decay)
{
int i;
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){
delta[j] = b.truth[i][j]-output[j];
if(output[j] > max) {
max = output[j];
max_k = j;
}
for(i = 0; i < d.X.rows; ++i){
int index = rand()%d.X.rows;
correct += 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));
}
if(b.truth[i][max_k]) ++correct;
printf("%f\n", (double)correct/(i+1));
learn_network(net, b.images[i].data);
update_network(net, .001);
}
return (double)correct/d.X.rows;
}
void train_network(network net, data d, double step, double momentum, double decay)
{
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], step, momentum, decay);
if(i%100 == 0){
visualize_network(net);
cvWaitKey(100);
cvWaitKey(10);
}
}
visualize_network(net);
print_network(net);
cvWaitKey(100);
printf("Accuracy: %f\n", (double)correct/b.n);
printf("Accuracy: %f\n", (double)correct/d.X.rows);
}
int get_network_output_size_layer(network net, int i)
@ -250,7 +273,7 @@ void print_network(network net)
{
int i,j;
for(i = 0; i < net.n; ++i){
double *output;
double *output = 0;
int n = 0;
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@ -283,3 +306,17 @@ void print_network(network net)
fprintf(stderr, "\n");
}
}
double network_accuracy(network net, data d)
{
int i;
int correct = 0;
int k = get_network_output_size(net);
for(i = 0; i < d.X.rows; ++i){
forward_network(net, d.X.vals[i]);
double *out = get_network_output(net);
int guess = max_index(out, k);
if(d.y.vals[i][guess]) ++correct;
}
return (double)correct/d.X.rows;
}