X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=mlp.git;a=blobdiff_plain;f=ann.cc;fp=ann.cc;h=758b62422c108f2fe4776091e26315313636bc41;hp=c3e9e98441b9954e7d81f6a2a3ed166d88902d21;hb=a3e1c52fc152cbbbcc2c8c675f14efc0e653d6fd;hpb=713c683d77fc94a4257c4031b0c51ef4669a3d4a

diff --git a/ann.cc b/ann.cc
index c3e9e98..758b624 100644
--- a/ann.cc
+++ b/ann.cc
@@ -1,8 +1,10 @@
 /*
  *  mlp-mnist is an implementation of a multi-layer neural network.
  *
- *  Copyright (c) 2008 Idiap Research Institute, http://www.idiap.ch/
- *  Written by Francois Fleuret <francois.fleuret@idiap.ch>
+ *  Copyright (c) 2006 École Polytechnique Fédérale de Lausanne,
+ *  http://www.epfl.ch
+ *
+ *  Written by Francois Fleuret <francois@fleuret.org>
  *
  *  This file is part of mlp-mnist.
  *
@@ -266,10 +268,10 @@ int main(int argc, char **argv) {
   ImageSet training_set, validation_set, test_set;
 
   if(nb_training_examples > 0)
-    training_set.extract_unused_pictures(image_set, nb_training_examples);
+    training_set.sample_among_unused_pictures(image_set, nb_training_examples);
 
   if(nb_validation_examples > 0)
-    validation_set.extract_unused_pictures(image_set, nb_validation_examples);
+    validation_set.sample_among_unused_pictures(image_set, nb_validation_examples);
 
   if(save_data && mlp) mlp->save_data();
 
@@ -304,7 +306,7 @@ int main(int argc, char **argv) {
   // Testing the perceptron ////////////////////////////////////////////
 
   if(nb_test_examples > 0) {
-    test_set.extract_unused_pictures(image_set, nb_test_examples);
+    test_set.sample_among_unused_pictures(image_set, nb_test_examples);
     cout << "Error rate " << mlp->error(&test_set) << " (" << mlp->classification_error(&test_set)*100 << "%)\n";
 
     // This is to test the analytical gradient