From 8c3694bc911bbeab63e75c18f920e0991a5fa877 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 07 Dec 2013 17:38:50 +0000
Subject: [PATCH] Ensemble
---
src/tests.c | 53 +++++++++++++++++++++++++++++++++++++++++++++--------
1 files changed, 45 insertions(+), 8 deletions(-)
diff --git a/src/tests.c b/src/tests.c
index d7d9389..0b9b5db 100644
--- a/src/tests.c
+++ b/src/tests.c
@@ -204,21 +204,57 @@
int count = 0;
double lr = .0005;
while(++count <= 1){
- double acc = train_network_sgd(net, train, lr, .9, .001);
- printf("Training Accuracy: %lf", acc);
+ double acc = train_network_sgd(net, train, 10000, lr, .9, .001);
+ printf("Training Accuracy: %lf\n", acc);
lr /= 2;
}
- /*
double train_acc = network_accuracy(net, train);
fprintf(stderr, "\nTRAIN: %f\n", train_acc);
double test_acc = network_accuracy(net, test);
fprintf(stderr, "TEST: %f\n\n", test_acc);
printf("%d, %f, %f\n", count, train_acc, test_acc);
- */
//end = clock();
//printf("Neural Net Learning: %lf seconds\n", (double)(end-start)/CLOCKS_PER_SEC);
}
+void test_ensemble()
+{
+ int i;
+ srand(888888);
+ data d = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
+ normalize_data_rows(d);
+ randomize_data(d);
+ data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
+ normalize_data_rows(test);
+ data train = d;
+ /*
+ data *split = split_data(d, 1, 10);
+ data train = split[0];
+ data test = split[1];
+ */
+ matrix prediction = make_matrix(test.y.rows, test.y.cols);
+ int n = 30;
+ for(i = 0; i < n; ++i){
+ int count = 0;
+ double lr = .0005;
+ network net = parse_network_cfg("nist.cfg");
+ while(++count <= 5){
+ double acc = train_network_sgd(net, train, train.X.rows, lr, .9, .001);
+ printf("Training Accuracy: %lf\n", acc);
+ lr /= 2;
+ }
+ matrix partial = network_predict_data(net, test);
+ double acc = matrix_accuracy(test.y, partial);
+ printf("Model Accuracy: %lf\n", acc);
+ matrix_add_matrix(partial, prediction);
+ acc = matrix_accuracy(test.y, prediction);
+ printf("Current Ensemble Accuracy: %lf\n", acc);
+ free_matrix(partial);
+ }
+ double acc = matrix_accuracy(test.y, prediction);
+ printf("Full Ensemble Accuracy: %lf\n", acc);
+}
+
void test_kernel_update()
{
srand(0);
@@ -283,7 +319,7 @@
void test_split()
{
data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
- data *split = cv_split_data(train, 0, 13);
+ data *split = split_data(train, 0, 13);
printf("%d, %d, %d\n", train.X.rows, split[0].X.rows, split[1].X.rows);
}
@@ -291,8 +327,9 @@
int main()
{
//test_kernel_update();
- test_split();
- // test_nist();
+ //test_split();
+ test_ensemble();
+ //test_nist();
//test_full();
//test_random_preprocess();
//test_random_classify();
@@ -307,6 +344,6 @@
//test_convolutional_layer();
//verify_convolutional_layer();
//test_color();
- cvWaitKey(0);
+ //cvWaitKey(0);
return 0;
}
--
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