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Training on VOC

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This commit is contained in:
Joseph Redmon
2014-02-14 10:26:31 -08:00
parent f7a17f82eb
commit 118bdd6f62
26 changed files with 501 additions and 240 deletions
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93
src/network.c
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@ -21,6 +21,77 @@ network make_network(int n)
return net;
}
void print_convolutional_cfg(FILE *fp, convolutional_layer *l)
{
int i;
fprintf(fp, "[convolutional]\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"filters=%d\n"
"size=%d\n"
"stride=%d\n"
"activation=%s\n",
l->h, l->w, l->c,
l->n, l->size, l->stride,
get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l)
{
int i;
fprintf(fp, "[connected]\n"
"input=%d\n"
"output=%d\n"
"activation=%s\n",
l->inputs, l->outputs,
get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
fprintf(fp, "\n\n");
}
void print_maxpool_cfg(FILE *fp, maxpool_layer *l)
{
fprintf(fp, "[maxpool]\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"stride=%d\n\n",
l->h, l->w, l->c,
l->stride);
}
void print_softmax_cfg(FILE *fp, softmax_layer *l)
{
fprintf(fp, "[softmax]\n"
"input=%d\n\n",
l->inputs);
}
void save_network(network net, char *filename)
{
FILE *fp = fopen(filename, "w");
if(!fp) file_error(filename);
int i;
for(i = 0; i < net.n; ++i)
{
if(net.types[i] == CONVOLUTIONAL)
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i]);
else if(net.types[i] == CONNECTED)
print_connected_cfg(fp, (connected_layer *)net.layers[i]);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i]);
else if(net.types[i] == SOFTMAX)
print_softmax_cfg(fp, (softmax_layer *)net.layers[i]);
}
fclose(fp);
}
void forward_network(network net, float *input)
{
int i;
@ -64,7 +135,7 @@ void update_network(network net, float step, float momentum, float decay)
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer, step, momentum, 0);
update_connected_layer(layer, step, momentum, decay);
}
}
}
@ -121,9 +192,11 @@ float calculate_error_network(network net, float *truth)
float *out = get_network_output(net);
int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){
printf("%f, ", out[i]);
delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
}
printf("\n");
return sum;
}
@ -173,25 +246,31 @@ float backward_network(network net, float *input, float *truth)
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
forward_network(net, x);
int class = get_predicted_class_network(net);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
//return (y[class]?1:0);
return error;
forward_network(net, x);
//int class = get_predicted_class_network(net);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
//return (y[class]?1:0);
return error;
}
float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
{
int i;
float error = 0;
int correct = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
float *y = d.y.vals[index];
int class = get_predicted_class_network(net);
correct += (y[class]?1:0);
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
//if((i+1)%10 == 0){
// printf("%d: %f\n", (i+1), (float)correct/(i+1));
//}
}
printf("Accuracy: %f\n",(float) correct/n);
return error/n;
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)