diff --git a/examples/dnn_metric_learning_ex.cpp b/examples/dnn_metric_learning_ex.cpp
index 1c9925fa2..54f2e6e8b 100644
--- a/examples/dnn_metric_learning_ex.cpp
+++ b/examples/dnn_metric_learning_ex.cpp
@@ -13,7 +13,6 @@
     other and images of different people are far apart.  Then in that vector
     space it's very easy to do face recognition with some kind of k-nearest
     neighbor classifier.  
-    
 
     To keep this example as simple as possible we won't do face recognition.
     Instead, we will create a very simple network and use it to learn a mapping
@@ -65,15 +64,20 @@ int main() try
     // vectors.
     using net_type = loss_metric<fc<2,input<matrix<double,0,1>>>>; 
     net_type net;
-    // Now setup the trainer and train the network using our data.
     dnn_trainer<net_type> trainer(net);
     trainer.set_learning_rate(0.1);
-    trainer.set_min_learning_rate(0.001);
-    trainer.set_mini_batch_size(128);
-    trainer.be_verbose();
-    trainer.set_iterations_without_progress_threshold(100);
-    trainer.train(samples, labels);
 
+    // It should be emphasized out that it's really important that each mini-batch contain
+    // multiple instances of each class of object.  This is because the metric learning
+    // algorithm needs to consider pairs of objects that should be close as well as pairs
+    // of objects that should be far apart during each training step.  Here we just keep
+    // training on the same small batch so this constraint is trivially satisfied.
+    while(trainer.get_learning_rate() >= 1e-4)
+        trainer.train_one_step(samples, labels);
+
+    // Wait for training threads to stop
+    trainer.get_net();
+    cout << "done training" << endl;
 
 
     // Run all the samples through the network to get their 2D vector embeddings.