mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

Stable on MNIST, about to change a lot

Browse Source
This commit is contained in:
Joseph Redmon 2014-01-23 11:24:37 -08:00
parent ad9dbfe164
commit 1d53b6414e
5 changed files with 33 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
full.cfg
View File

@ -10,10 +10,6 @@ activation=ramp
[maxpool]
stride=2
[conn]
output = 100
activation=ramp
[conn]
output = 2
activation=ramp

4
nist.cfg
View File

@ -2,7 +2,7 @@
width=28
height=28
channels=1
filters=5
filters=20
size=5
stride=1
activation=ramp
@ -20,7 +20,7 @@ activation=ramp
stride=2
[conn]
output = 100
output = 500
activation=ramp
[conn]

18
src/network.c
View File

@ -187,6 +187,24 @@ double train_network_sgd(network net, data d, int n, double step, double momentu
}
return (double)correct/n;
}
double train_network_batch(network net, data d, int n, double step, double momentum,double decay)
{
int i;
int correct = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
double *x = d.X.vals[index];
double *y = d.y.vals[index];
forward_network(net, x);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
correct += (y[class]?1:0);
}
update_network(net, step, momentum, decay);
return (double)correct/n;
}
void train_network(network net, data d, double step, double momentum, double decay)
{

1
src/network.h
View File

@ -25,6 +25,7 @@ void forward_network(network net, double *input);
void backward_network(network net, double *input, double *truth);
void update_network(network net, double step, double momentum, double decay);
double train_network_sgd(network net, data d, int n, double step, double momentum,double decay);
double train_network_batch(network net, data d, int n, double step, double momentum,double decay);
void train_network(network net, data d, double step, double momentum, double decay);
matrix network_predict_data(network net, data test);
double network_accuracy(network net, data d);

17
src/tests.c
View File

@ -184,9 +184,12 @@ void test_full()
srand(0);
int i = 0;
char *labels[] = {"cat","dog"};
double lr = .00001;
double momentum = .9;
double decay = 0.01;
while(i++ < 1000 || 1){
data train = load_data_image_pathfile_random("train_paths.txt", 1000, labels, 2);
train_network(net, train, .0005, 0, 0);
train_network(net, train, lr, momentum, decay);
free_data(train);
printf("Round %d\n", i);
}
@ -206,9 +209,13 @@ void test_nist()
double lr = .0005;
double momentum = .9;
double decay = 0.01;
clock_t start = clock(), end;
while(++count <= 1000){
double acc = train_network_sgd(net, train, 1000, lr, momentum, decay);
printf("Training Accuracy: %lf, Params: %f %f %f\n", acc, lr, momentum, decay);
double acc = train_network_sgd(net, train, 6400, lr, momentum, decay);
printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, 1.-acc, lr, momentum, decay);
end = clock();
printf("Time: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
start=end;
visualize_network(net);
cvWaitKey(100);
//lr /= 2;
@ -334,8 +341,8 @@ int main()
{
//test_kernel_update();
//test_split();
test_ensemble();
//test_nist();
//test_ensemble();
test_nist();
//test_full();
//test_random_preprocess();
//test_random_classify();