Improved example

examples/dnn_metric_learning_on_images_ex.cpp

@@ -288,8 +288,12 @@ int main(int argc, char** argv)
     dlib::rand rnd(time(0));
     load_mini_batch(5, 5, rnd, objs, images, labels);
 
+    // Normally you would use the non-batch-normalized version of the network to do
+    // testing, which is what we do here.
+    anet_type testing_net = net;
+
     // Run all the images through the network to get their vector embeddings.
-    std::vector<matrix<float,0,1>> embedded = net(images);
+    std::vector<matrix<float,0,1>> embedded = testing_net(images);
 
     // Now, check if the embedding puts images with the same labels near each other and
     // images with different labels far apart.
@@ -304,14 +308,14 @@ int main(int argc, char** argv)
                 // The loss_metric layer will cause images with the same label to be less
                 // than net.loss_details().get_distance_threshold() distance from each
                 // other.  So we can use that distance value as our testing threshold.
-                if (length(embedded[i]-embedded[j]) < net.loss_details().get_distance_threshold())
+                if (length(embedded[i]-embedded[j]) < testing_net.loss_details().get_distance_threshold())
                     ++num_right;
                 else
                     ++num_wrong;
             }
             else
             {
-                if (length(embedded[i]-embedded[j]) >= net.loss_details().get_distance_threshold())
+                if (length(embedded[i]-embedded[j]) >= testing_net.loss_details().get_distance_threshold())
                     ++num_right;
                 else
                     ++num_wrong;