diff --git a/examples/dnn_mnist_resnet_ex.cpp b/examples/dnn_mnist_resnet_ex.cpp
index e9b4bfde6..719a57a6c 100644
--- a/examples/dnn_mnist_resnet_ex.cpp
+++ b/examples/dnn_mnist_resnet_ex.cpp
@@ -60,8 +60,7 @@ int main(int argc, char** argv) try
                 );
 
 
-    //dnn_trainer<net_type,adam> trainer(net,adam(0.001));
-    dnn_trainer<net_type> trainer(net,sgd(0.1));
+    dnn_trainer<net_type,adam> trainer(net,adam(0.001));
     trainer.be_verbose();
     trainer.set_synchronization_file("mnist_resnet_sync", std::chrono::seconds(100));
     std::vector<matrix<unsigned char>> mini_batch_samples;
@@ -86,11 +85,29 @@ int main(int argc, char** argv) try
     // wait for threaded processing to stop.
     trainer.get_net();
 
+    // You can access sub layers of the network like this:
+    net.subnet().subnet().get_output();
+    layer<avg_pool>(net).get_output();
+
     net.clean();
-    serialize("mnist_network.dat") << net;
+    serialize("mnist_res_network.dat") << net;
+
+
+
+    typedef loss_multiclass_log<fc<avg_pool<
+                                ares<ares<ares<ares<
+                                repeat<10,ares,
+                                ares<
+                                ares<
+                                input<matrix<unsigned char>
+                                >>>>>>>>>>> test_net_type;
+    test_net_type tnet = net;
+    // or you could deserialize the saved network
+    deserialize("mnist_res_network.dat") >> tnet;
+
 
     // Run the net on all the data to get predictions
-    std::vector<unsigned long> predicted_labels = net(training_images);
+    std::vector<unsigned long> predicted_labels = tnet(training_images);
     int num_right = 0;
     int num_wrong = 0;
     for (size_t i = 0; i < training_images.size(); ++i)
@@ -105,7 +122,7 @@ int main(int argc, char** argv) try
     cout << "training num_wrong: " << num_wrong << endl;
     cout << "training accuracy:  " << num_right/(double)(num_right+num_wrong) << endl;
 
-    predicted_labels = net(testing_images);
+    predicted_labels = tnet(testing_images);
     num_right = 0;
     num_wrong = 0;
     for (size_t i = 0; i < testing_images.size(); ++i)