From d1965bdb969920c85f72785ec6e1f3d7bda957de Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 14 Mar 2016 06:18:42 +0000
Subject: [PATCH] Go
---
src/cifar.c | 163 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 162 insertions(+), 1 deletions(-)
diff --git a/src/cifar.c b/src/cifar.c
index f887877..de52bb8 100644
--- a/src/cifar.c
+++ b/src/cifar.c
@@ -33,7 +33,7 @@
float loss = train_network_sgd(net, train, 1);
if(avg_loss == -1) avg_loss = loss;
- avg_loss = avg_loss*.9 + loss*.1;
+ avg_loss = avg_loss*.95 + loss*.05;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
if(*net.seen/N > epoch){
epoch = *net.seen/N;
@@ -57,6 +57,95 @@
free_data(train);
}
+void train_cifar_distill(char *cfgfile, char *weightfile)
+{
+ data_seed = time(0);
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+
+ char *backup_directory = "/home/pjreddie/backup/";
+ int classes = 10;
+ int N = 50000;
+
+ char **labels = get_labels("data/cifar/labels.txt");
+ int epoch = (*net.seen)/N;
+
+ data train = load_all_cifar10();
+ matrix soft = csv_to_matrix("results/ensemble.csv");
+
+ float weight = .9;
+ scale_matrix(soft, weight);
+ scale_matrix(train.y, 1. - weight);
+ matrix_add_matrix(soft, train.y);
+
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ clock_t time=clock();
+
+ float loss = train_network_sgd(net, train, 1);
+ if(avg_loss == -1) avg_loss = loss;
+ avg_loss = avg_loss*.95 + loss*.05;
+ printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
+ if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ save_weights(net, buff);
+ }
+ if(get_current_batch(net)%100 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup",backup_directory,base);
+ save_weights(net, buff);
+ }
+ }
+ char buff[256];
+ sprintf(buff, "%s/%s.weights", backup_directory, base);
+ save_weights(net, buff);
+
+ free_network(net);
+ free_ptrs((void**)labels, classes);
+ free(base);
+ free_data(train);
+}
+
+void test_cifar_multi(char *filename, char *weightfile)
+{
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(time(0));
+
+ float avg_acc = 0;
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+ int i;
+ for(i = 0; i < test.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, test.X.vals[i]);
+
+ float pred[10] = {0};
+
+ float *p = network_predict(net, im.data);
+ axpy_cpu(10, 1, p, 1, pred, 1);
+ flip_image(im);
+ p = network_predict(net, im.data);
+ axpy_cpu(10, 1, p, 1, pred, 1);
+
+ int index = max_index(pred, 10);
+ int class = max_index(test.y.vals[i], 10);
+ if(index == class) avg_acc += 1;
+ free_image(im);
+ printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
+ }
+}
+
void test_cifar(char *filename, char *weightfile)
{
network net = parse_network_cfg(filename);
@@ -79,6 +168,73 @@
free_data(test);
}
+void test_cifar_csv(char *filename, char *weightfile)
+{
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+ matrix pred = network_predict_data(net, test);
+
+ int i;
+ for(i = 0; i < test.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, test.X.vals[i]);
+ flip_image(im);
+ }
+ matrix pred2 = network_predict_data(net, test);
+ scale_matrix(pred, .5);
+ scale_matrix(pred2, .5);
+ matrix_add_matrix(pred2, pred);
+
+ matrix_to_csv(pred);
+ fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+ free_data(test);
+}
+
+void test_cifar_csvtrain(char *filename, char *weightfile)
+{
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ data test = load_all_cifar10();
+
+ matrix pred = network_predict_data(net, test);
+
+ int i;
+ for(i = 0; i < test.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, test.X.vals[i]);
+ flip_image(im);
+ }
+ matrix pred2 = network_predict_data(net, test);
+ scale_matrix(pred, .5);
+ scale_matrix(pred2, .5);
+ matrix_add_matrix(pred2, pred);
+
+ matrix_to_csv(pred);
+ fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+ free_data(test);
+}
+
+void eval_cifar_csv()
+{
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+ matrix pred = csv_to_matrix("results/combined.csv");
+ fprintf(stderr, "%d %d\n", pred.rows, pred.cols);
+
+ fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+ free_data(test);
+ free_matrix(pred);
+}
+
+
void run_cifar(int argc, char **argv)
{
if(argc < 4){
@@ -89,7 +245,12 @@
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
if(0==strcmp(argv[2], "train")) train_cifar(cfg, weights);
+ else if(0==strcmp(argv[2], "distill")) train_cifar_distill(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_cifar(cfg, weights);
+ else if(0==strcmp(argv[2], "multi")) test_cifar_multi(cfg, weights);
+ else if(0==strcmp(argv[2], "csv")) test_cifar_csv(cfg, weights);
+ else if(0==strcmp(argv[2], "csvtrain")) test_cifar_csvtrain(cfg, weights);
+ else if(0==strcmp(argv[2], "eval")) eval_cifar_csv();
}
--
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