diff --git a/.github/workflows/ubuntu.yml b/.github/workflows/ubuntu.yml
index 02048e328..b33be7df4 100644
--- a/.github/workflows/ubuntu.yml
+++ b/.github/workflows/ubuntu.yml
@@ -124,6 +124,7 @@ jobs:
     - name: Build tests
       working-directory: ${{ github.workspace }}/test
       run: |
+        echo `pwd`
         cmake . -B ${{ env.build_dir }} -DCMAKE_BUILD_TYPE=${{ env.config }} -G Ninja
         cmake --build .
 
diff --git a/dlib/test/CMakeLists.txt b/dlib/test/CMakeLists.txt
index 8d05fdce5..4536b7cb9 100644
--- a/dlib/test/CMakeLists.txt
+++ b/dlib/test/CMakeLists.txt
@@ -17,147 +17,147 @@ add_subdirectory(.. dlib_build)
 # This variable contains a list of all the tests we are building
 # into the regression test suite.
 set (tests
-   example.cpp
-   active_learning.cpp
-   any.cpp
-   any_function.cpp
-   array2d.cpp
-   array.cpp
-   assignment_learning.cpp
-   base64.cpp
-   bayes_nets.cpp
-   bigint.cpp
-   binary_search_tree_kernel_1a.cpp
-   binary_search_tree_kernel_2a.cpp
-   binary_search_tree_mm1.cpp
-   binary_search_tree_mm2.cpp
-   bridge.cpp
-   bsp.cpp
-   byte_orderer.cpp
-   cca.cpp
-   clustering.cpp
-   cmd_line_parser.cpp
-   cmd_line_parser_wchar_t.cpp
-   compress_stream.cpp
-   conditioning_class_c.cpp
-   conditioning_class.cpp
-   config_reader.cpp
-   correlation_tracker.cpp
-   crc32.cpp
-   create_iris_datafile.cpp
-   data_io.cpp
-   directed_graph.cpp
-   discriminant_pca.cpp
-   disjoint_subsets.cpp
-   disjoint_subsets_sized.cpp
-   ekm_and_lisf.cpp
-   empirical_kernel_map.cpp
-   entropy_coder.cpp
-   entropy_encoder_model.cpp
-   example_args.cpp
-   face.cpp
-   fft.cpp
-   fhog.cpp
-   filtering.cpp
-   find_max_factor_graph_nmplp.cpp
-   find_max_factor_graph_viterbi.cpp
-   geometry.cpp
-   graph.cpp
-   graph_cuts.cpp
-   graph_labeler.cpp
-   hash.cpp
-   hash_map.cpp
-   hash_set.cpp
-   hash_table.cpp
-   hog_image.cpp
-   image.cpp
-   iosockstream.cpp
-   is_same_object.cpp
-   isotonic_regression.cpp
-   kcentroid.cpp
-   kernel_matrix.cpp
-   kmeans.cpp
-   learning_to_track.cpp
-   least_squares.cpp
-   linear_manifold_regularizer.cpp
-   lspi.cpp
-   lz77_buffer.cpp
-   map.cpp
-   matrix2.cpp
-   matrix3.cpp
-   matrix4.cpp
-   matrix_chol.cpp
-   matrix.cpp
-   matrix_eig.cpp
-   matrix_lu.cpp
-   matrix_qr.cpp
-   max_cost_assignment.cpp
-   max_sum_submatrix.cpp
-   md5.cpp
-   member_function_pointer.cpp
-   metaprogramming.cpp
-   mpc.cpp
-   multithreaded_object.cpp
-   numerical_integration.cpp
-   object_detector.cpp
-   oca.cpp
-   one_vs_all_trainer.cpp
-   one_vs_one_trainer.cpp
-   optimization.cpp
-   optimization_test_functions.cpp
-   global_optimization.cpp
-   opt_qp_solver.cpp
-   parallel_for.cpp
-   parse.cpp
-   pipe.cpp
-   pixel.cpp
-   probabilistic.cpp
-   pyramid_down.cpp
-   queue.cpp
-   rand.cpp
-   ranking.cpp
-   read_write_mutex.cpp
-   reference_counter.cpp
-   rls.cpp
-   random_forest.cpp
-   sammon.cpp
-   scan_image.cpp
-   sequence.cpp
-   sequence_labeler.cpp
-   sequence_segmenter.cpp
-   serialize.cpp
-   set.cpp
-   sldf.cpp
-   sliding_buffer.cpp
-   sockets2.cpp
-   sockets.cpp
-   sockstreambuf.cpp
-   sparse_vector.cpp
-   stack.cpp
-   static_map.cpp
-   static_set.cpp
-   statistics.cpp
-   std_vector_c.cpp
-   string.cpp
-   svm_c_linear.cpp
-   svm_c_linear_dcd.cpp
-   svm.cpp
-   svm_multiclass_linear.cpp
-   svm_struct.cpp
-   svr_linear_trainer.cpp
-   symmetric_matrix_cache.cpp
-   thread_pool.cpp
-   threads.cpp
-   timer.cpp
-   tokenizer.cpp
-   trust_region.cpp
-   tuple.cpp
-   type_safe_union.cpp
-   vectorstream.cpp
+   # example.cpp
+   # active_learning.cpp
+   # any.cpp
+   # any_function.cpp
+   # array2d.cpp
+   # array.cpp
+   # assignment_learning.cpp
+   # base64.cpp
+   # bayes_nets.cpp
+   # bigint.cpp
+   # binary_search_tree_kernel_1a.cpp
+   # binary_search_tree_kernel_2a.cpp
+   # binary_search_tree_mm1.cpp
+   # binary_search_tree_mm2.cpp
+   # bridge.cpp
+   # bsp.cpp
+   # byte_orderer.cpp
+   # cca.cpp
+   # clustering.cpp
+   # cmd_line_parser.cpp
+   # cmd_line_parser_wchar_t.cpp
+   # compress_stream.cpp
+   # conditioning_class_c.cpp
+   # conditioning_class.cpp
+   # config_reader.cpp
+   # correlation_tracker.cpp
+   # crc32.cpp
+   # create_iris_datafile.cpp
+   # data_io.cpp
+   # directed_graph.cpp
+   # discriminant_pca.cpp
+   # disjoint_subsets.cpp
+   # disjoint_subsets_sized.cpp
+   # ekm_and_lisf.cpp
+   # empirical_kernel_map.cpp
+   # entropy_coder.cpp
+   # entropy_encoder_model.cpp
+   # example_args.cpp
+   # face.cpp
+   # fft.cpp
+   # fhog.cpp
+   # filtering.cpp
+   # find_max_factor_graph_nmplp.cpp
+   # find_max_factor_graph_viterbi.cpp
+   # geometry.cpp
+   # graph.cpp
+   # graph_cuts.cpp
+   # graph_labeler.cpp
+   # hash.cpp
+   # hash_map.cpp
+   # hash_set.cpp
+   # hash_table.cpp
+   # hog_image.cpp
+   # image.cpp
+   # iosockstream.cpp
+   # is_same_object.cpp
+   # isotonic_regression.cpp
+   # kcentroid.cpp
+   # kernel_matrix.cpp
+   # kmeans.cpp
+   # learning_to_track.cpp
+   # least_squares.cpp
+   # linear_manifold_regularizer.cpp
+   # lspi.cpp
+   # lz77_buffer.cpp
+   # map.cpp
+   # matrix2.cpp
+   # matrix3.cpp
+   # matrix4.cpp
+   # matrix_chol.cpp
+   # matrix.cpp
+   # matrix_eig.cpp
+   # matrix_lu.cpp
+   # matrix_qr.cpp
+   # max_cost_assignment.cpp
+   # max_sum_submatrix.cpp
+   # md5.cpp
+   # member_function_pointer.cpp
+   # metaprogramming.cpp
+   # mpc.cpp
+   # multithreaded_object.cpp
+   # numerical_integration.cpp
+   # object_detector.cpp
+   # oca.cpp
+   # one_vs_all_trainer.cpp
+   # one_vs_one_trainer.cpp
+   # optimization.cpp
+   # optimization_test_functions.cpp
+   # global_optimization.cpp
+   # opt_qp_solver.cpp
+   # parallel_for.cpp
+   # parse.cpp
+   # pipe.cpp
+   # pixel.cpp
+   # probabilistic.cpp
+   # pyramid_down.cpp
+   # queue.cpp
+   # rand.cpp
+   # ranking.cpp
+   # read_write_mutex.cpp
+   # reference_counter.cpp
+   # rls.cpp
+   # random_forest.cpp
+   # sammon.cpp
+   # scan_image.cpp
+   # sequence.cpp
+   # sequence_labeler.cpp
+   # sequence_segmenter.cpp
+   # serialize.cpp
+   # set.cpp
+   # sldf.cpp
+   # sliding_buffer.cpp
+   # sockets2.cpp
+   # sockets.cpp
+   # sockstreambuf.cpp
+   # sparse_vector.cpp
+   # stack.cpp
+   # static_map.cpp
+   # static_set.cpp
+   # statistics.cpp
+   # std_vector_c.cpp
+   # string.cpp
+   # svm_c_linear.cpp
+   # svm_c_linear_dcd.cpp
+   # svm.cpp
+   # svm_multiclass_linear.cpp
+   # svm_struct.cpp
+   # svr_linear_trainer.cpp
+   # symmetric_matrix_cache.cpp
+   # thread_pool.cpp
+   # threads.cpp
+   # timer.cpp
+   # tokenizer.cpp
+   # trust_region.cpp
+   # tuple.cpp
+   # type_safe_union.cpp
+   # vectorstream.cpp
    dnn.cpp
-   cublas.cpp
-   find_optimal_parameters.cpp
-   elastic_net.cpp
+   # cublas.cpp
+   # find_optimal_parameters.cpp
+   # elastic_net.cpp
    )
 
 
@@ -170,7 +170,7 @@ if (CMAKE_COMPILER_IS_GNUCXX)
    add_definitions("-W -Wall")
    # I don't care about unused testing functions though.  I like to keep them
    # around.  Don't warn about it.
-   add_definitions("-Wno-unused-function")
+   add_definitions("-Wno-unused-function -Wno-deprecated-copy -fdiagnostics-color=always")
 endif()
 
 
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 893f8a3c7..8c538beee 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -36,6 +36,7 @@ cmake_minimum_required(VERSION 2.8.12)
 # Every project needs a name.  We call this the "examples" project.
 project(examples)
 
+add_compile_options (-fdiagnostics-color=always)
 
 # Tell cmake we will need dlib.  This command will pull in dlib and compile it
 # into your project.  Note that you don't need to compile or install dlib.  All
@@ -60,10 +61,10 @@ add_subdirectory(../dlib dlib_build)
 # are going to compile one of the dlib example programs which has only one .cpp
 # file, assignment_learning_ex.cpp.  If your program consisted of multiple .cpp
 # files you would simply list them here in the add_executable() statement.  
-add_executable(assignment_learning_ex assignment_learning_ex.cpp)
+# add_executable(assignment_learning_ex assignment_learning_ex.cpp)
 # Finally, you need to tell CMake that this program, assignment_learning_ex,
 # depends on dlib.  You do that with this statement: 
-target_link_libraries(assignment_learning_ex dlib::dlib)
+# target_link_libraries(assignment_learning_ex dlib::dlib)
 
 
 
@@ -134,132 +135,135 @@ endmacro()
 # like this:
 #   cmake .. -G "Visual Studio 14 2015 Win64" -T host=x64 
 if (NOT USING_OLD_VISUAL_STUDIO_COMPILER)
-   add_example(dnn_metric_learning_ex)
-   add_gui_example(dnn_face_recognition_ex)
+   # add_example(dnn_metric_learning_ex)
+   # add_gui_example(dnn_face_recognition_ex)
    add_example(dnn_introduction_ex)
-   add_example(dnn_introduction2_ex)
-   add_example(dnn_introduction3_ex)
-   add_example(dnn_inception_ex)
-   add_gui_example(dnn_mmod_ex)
-   add_gui_example(dnn_mmod_face_detection_ex)
-   add_gui_example(random_cropper_ex)
-   add_gui_example(dnn_mmod_dog_hipsterizer)
+   # add_example(dnn_introduction2_ex)
+   # add_example(dnn_introduction3_ex)
+   # add_example(dnn_inception_ex)
+   # add_gui_example(dnn_mmod_ex)
+   # add_gui_example(dnn_mmod_face_detection_ex)
+   # add_gui_example(random_cropper_ex)
+   # add_gui_example(dnn_mmod_dog_hipsterizer)
    add_gui_example(dnn_imagenet_ex)
-   add_gui_example(dnn_mmod_find_cars_ex)
-   add_gui_example(dnn_mmod_find_cars2_ex)
-   add_example(dnn_mmod_train_find_cars_ex)
-   add_gui_example(dnn_semantic_segmentation_ex)
-   add_gui_example(dnn_instance_segmentation_ex)
-   add_example(dnn_imagenet_train_ex)
-   add_example(dnn_semantic_segmentation_train_ex)
-   add_example(dnn_instance_segmentation_train_ex)
-   add_example(dnn_metric_learning_on_images_ex)
-   add_gui_example(dnn_dcgan_train_ex)
+   # add_gui_example(dnn_mmod_find_cars_ex)
+   # add_gui_example(dnn_mmod_find_cars2_ex)
+   # add_example(dnn_mmod_train_find_cars_ex)
+   # add_gui_example(dnn_semantic_segmentation_ex)
+   # add_gui_example(dnn_instance_segmentation_ex)
+   # add_example(dnn_imagenet_train_ex)
+   # add_example(dnn_semantic_segmentation_train_ex)
+   # add_example(dnn_instance_segmentation_train_ex)
+   # add_example(dnn_metric_learning_on_images_ex)
+   # add_gui_example(dnn_dcgan_train_ex)
+   # add_gui_example(dnn_neural_style_transfer_ex)
 endif()
 
 
-if (DLIB_NO_GUI_SUPPORT)
-   message("No GUI support, so we won't build the webcam_face_pose_ex example.")
-else()
-   find_package(OpenCV QUIET)
-   if (OpenCV_FOUND)
-      include_directories(${OpenCV_INCLUDE_DIRS})
+# if (DLIB_NO_GUI_SUPPORT)
+#    message("No GUI support, so we won't build the webcam_face_pose_ex example.")
+# else()
+#    find_package(OpenCV QUIET)
+#    if (OpenCV_FOUND)
+#       include_directories(${OpenCV_INCLUDE_DIRS})
 
-      add_executable(webcam_face_pose_ex webcam_face_pose_ex.cpp)
-      target_link_libraries(webcam_face_pose_ex dlib::dlib ${OpenCV_LIBS} )
-   else()
-      message("OpenCV not found, so we won't build the webcam_face_pose_ex example.")
-   endif()
-endif()
+#       add_executable(webcam_face_pose_ex webcam_face_pose_ex.cpp)
+#       target_link_libraries(webcam_face_pose_ex dlib::dlib ${OpenCV_LIBS} )
+#    else()
+#       message("OpenCV not found, so we won't build the webcam_face_pose_ex example.")
+#    endif()
+# endif()
 
 
 
-#here we apply our macros 
-add_gui_example(3d_point_cloud_ex)
-add_example(bayes_net_ex)
-add_example(bayes_net_from_disk_ex)
-add_gui_example(bayes_net_gui_ex)
-add_example(bridge_ex)
-add_example(bsp_ex)
-add_example(compress_stream_ex)
-add_example(config_reader_ex)
-add_example(custom_trainer_ex)
-add_example(dir_nav_ex)
-add_example(empirical_kernel_map_ex)
-add_gui_example(face_detection_ex)
-add_gui_example(face_landmark_detection_ex)
-add_gui_example(fhog_ex)
-add_gui_example(fhog_object_detector_ex)
-add_example(file_to_code_ex)
-add_example(graph_labeling_ex)
+# #here we apply our macros 
+# add_gui_example(3d_point_cloud_ex)
+# add_example(bayes_net_ex)
+# add_example(bayes_net_from_disk_ex)
+# add_gui_example(bayes_net_gui_ex)
+# add_example(bridge_ex)
+# add_example(bsp_ex)
+# add_example(compress_stream_ex)
+# add_example(config_reader_ex)
+# add_example(custom_trainer_ex)
+# add_example(dir_nav_ex)
+# add_example(empirical_kernel_map_ex)
+# add_gui_example(face_detection_ex)
+# add_gui_example(face_landmark_detection_ex)
+# add_gui_example(fhog_ex)
+# add_gui_example(fhog_object_detector_ex)
+# add_example(file_to_code_ex)
+# add_example(graph_labeling_ex)
 add_gui_example(gui_api_ex)
-add_gui_example(hough_transform_ex)
-add_gui_example(image_ex)
-add_example(integrate_function_adapt_simp_ex)
-add_example(iosockstream_ex)
-add_example(kcentroid_ex)
-add_example(kkmeans_ex)
-add_example(krls_ex)
-add_example(krls_filter_ex)
-add_example(krr_classification_ex)
-add_example(krr_regression_ex)
-add_example(learning_to_track_ex)
-add_example(least_squares_ex)
-add_example(linear_manifold_regularizer_ex)
-add_example(logger_custom_output_ex)
-add_example(logger_ex)
-add_example(logger_ex_2)
-add_example(matrix_ex)
-add_example(matrix_expressions_ex)
-add_example(max_cost_assignment_ex)
-add_example(member_function_pointer_ex)
-add_example(mlp_ex)
-add_example(model_selection_ex)
-add_gui_example(mpc_ex)
-add_example(multiclass_classification_ex)
-add_example(multithreaded_object_ex)
-add_gui_example(object_detector_advanced_ex)
-add_gui_example(object_detector_ex)
-add_gui_example(one_class_classifiers_ex)
-add_example(optimization_ex)
-add_example(parallel_for_ex)
-add_example(pipe_ex)
-add_example(pipe_ex_2)
-add_example(quantum_computing_ex)
-add_example(queue_ex)
-add_example(rank_features_ex)
-add_example(running_stats_ex)
-add_example(rvm_ex)
-add_example(rvm_regression_ex)
-add_example(sequence_labeler_ex)
-add_example(sequence_segmenter_ex)
-add_example(server_http_ex)
-add_example(server_iostream_ex)
-add_example(sockets_ex)
-add_example(sockstreambuf_ex)
-add_example(std_allocator_ex)
-add_gui_example(surf_ex)
-add_example(svm_c_ex)
-add_example(svm_ex)
-add_example(svm_pegasos_ex)
-add_example(svm_rank_ex)
-add_example(svm_sparse_ex)
-add_example(svm_struct_ex)
-add_example(svr_ex)
-add_example(thread_function_ex)
-add_example(thread_pool_ex)
-add_example(threaded_object_ex)
-add_example(threads_ex)
-add_example(timer_ex)
-add_gui_example(train_object_detector)
-add_example(train_shape_predictor_ex)
-add_example(using_custom_kernels_ex)
-add_gui_example(video_tracking_ex)
-add_example(xml_parser_ex)
+# add_gui_example(hough_transform_ex)
+# add_gui_example(image_ex)
+# add_example(integrate_function_adapt_simp_ex)
+# add_example(iosockstream_ex)
+# add_example(kcentroid_ex)
+# add_example(kkmeans_ex)
+# add_example(krls_ex)
+# add_example(krls_filter_ex)
+# add_example(krr_classification_ex)
+# add_example(krr_regression_ex)
+# add_example(learning_to_track_ex)
+# add_example(least_squares_ex)
+# add_example(linear_manifold_regularizer_ex)
+# add_example(logger_custom_output_ex)
+# add_example(logger_ex)
+# add_example(logger_ex_2)
+# add_example(matrix_ex)
+# add_example(matrix_expressions_ex)
+# add_example(max_cost_assignment_ex)
+# add_example(member_function_pointer_ex)
+# add_example(mlp_ex)
+# add_example(model_selection_ex)
+# add_gui_example(mpc_ex)
+# add_example(multiclass_classification_ex)
+# add_example(multithreaded_object_ex)
+# add_gui_example(object_detector_advanced_ex)
+# add_gui_example(object_detector_ex)
+# add_gui_example(one_class_classifiers_ex)
+# add_example(optimization_ex)
+# add_example(parallel_for_ex)
+# add_example(pipe_ex)
+# add_example(pipe_ex_2)
+# add_example(quantum_computing_ex)
+# add_example(queue_ex)
+# add_example(rank_features_ex)
+# add_example(running_stats_ex)
+# add_example(rvm_ex)
+# add_example(rvm_regression_ex)
+# add_example(sequence_labeler_ex)
+# add_example(sequence_segmenter_ex)
+# add_example(server_http_ex)
+# add_example(server_iostream_ex)
+# add_example(sockets_ex)
+# add_example(sockstreambuf_ex)
+# add_example(std_allocator_ex)
+# add_gui_example(surf_ex)
+# add_example(svm_c_ex)
+# add_example(svm_ex)
+# add_example(svm_pegasos_ex)
+# add_example(svm_rank_ex)
+# add_example(svm_sparse_ex)
+# add_example(svm_struct_ex)
+# add_example(svr_ex)
+# add_example(thread_function_ex)
+# add_example(thread_pool_ex)
+# add_example(threaded_object_ex)
+# add_example(threads_ex)
+# add_example(timer_ex)
+# add_gui_example(train_object_detector)
+# add_example(train_shape_predictor_ex)
+# add_example(using_custom_kernels_ex)
+# add_gui_example(video_tracking_ex)
+# add_example(xml_parser_ex)
+# add_example(dnn_graph_visitor_ex)
+add_example(playground)
 
 
-if (DLIB_LINK_WITH_SQLITE3)
-   add_example(sqlite_ex)
-endif()
+# if (DLIB_LINK_WITH_SQLITE3)
+#    add_example(sqlite_ex)
+# endif()
 
 
diff --git a/examples/dnn_imagenet_ex.cpp b/examples/dnn_imagenet_ex.cpp
index d1fa82823..bc87d10bb 100644
--- a/examples/dnn_imagenet_ex.cpp
+++ b/examples/dnn_imagenet_ex.cpp
@@ -13,7 +13,7 @@
     For an introduction to dlib's DNN module read the dnn_introduction_ex.cpp and
     dnn_introduction2_ex.cpp example programs.
 
-    
+
     Finally, these tools will use CUDA and cuDNN to drastically accelerate
     network training and testing.  CMake should automatically find them if they
     are installed and configure things appropriately.  If not, the program will
@@ -30,7 +30,7 @@
 
 using namespace std;
 using namespace dlib;
- 
+
 // ----------------------------------------------------------------------------------------
 
 // This block of statements defines the resnet-34 network
@@ -41,7 +41,7 @@ using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;
 template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
 using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>;
 
-template <int N, template <typename> class BN, int stride, typename SUBNET> 
+template <int N, template <typename> class BN, int stride, typename SUBNET>
 using block  = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;
 
 template <int N, typename SUBNET> using ares      = relu<residual<block,N,affine,SUBNET>>;
@@ -130,7 +130,7 @@ int main(int argc, char** argv) try
     // already does this.  But if we instead want to get the probability of each
     // class as output we need to replace the last layer of the network with a
     // softmax layer, which we do as follows:
-    softmax<anet_type::subnet_type> snet; 
+    softmax<anet_type::subnet_type> snet;
     snet.subnet() = net.subnet();
 
     dlib::array<matrix<rgb_pixel>> images;
@@ -150,17 +150,24 @@ int main(int argc, char** argv) try
         // p(i) == the probability the image contains object of class i.
         matrix<float,1,1000> p = sum_rows(mat(snet(images.begin(), images.end())))/num_crops;
 
-        win.set_image(img);
+        // win.set_image(img);
+        bool keep = false;
         // Print the 5 most probable labels
         for (int k = 0; k < 5; ++k)
         {
             unsigned long predicted_label = index_of_max(p);
-            cout << p(predicted_label) << ": " << labels[predicted_label] << endl;
+            // cout << p(predicted_label) << ": " << labels[predicted_label] << endl;
             p(predicted_label) = 0;
+            if (labels[predicted_label] == "racket" or labels[predicted_label] == "tennis_ball")
+                keep = true;
         }
-
-        cout << "Hit enter to process the next image";
-        cin.get();
+        if (not keep)
+        {
+            std::remove(argv[i]);
+            cout << "removing " << argv[i] << '\n';
+        }
+        // cout << "Hit enter to process the next image";
+        // cin.get();
     }
 
 }
diff --git a/examples/dnn_introduction_ex.cpp b/examples/dnn_introduction_ex.cpp
index 556da28ff..c6b3d6d81 100644
--- a/examples/dnn_introduction_ex.cpp
+++ b/examples/dnn_introduction_ex.cpp
@@ -9,7 +9,7 @@
     The specific network we will run is from the paper
         LeCun, Yann, et al. "Gradient-based learning applied to document recognition."
         Proceedings of the IEEE 86.11 (1998): 2278-2324.
-    except that we replace the sigmoid non-linearities with rectified linear units. 
+    except that we replace the sigmoid non-linearities with rectified linear units.
 
     These tools will use CUDA and cuDNN to drastically accelerate network
     training and testing.  CMake should automatically find them if they are
@@ -24,10 +24,10 @@
 
 using namespace std;
 using namespace dlib;
- 
+
 int main(int argc, char** argv) try
 {
-    // This example is going to run on the MNIST dataset.  
+    // This example is going to run on the MNIST dataset.
     if (argc != 2)
     {
         cout << "This example needs the MNIST dataset to run!" << endl;
@@ -50,8 +50,8 @@ int main(int argc, char** argv) try
 
     // Now let's define the LeNet.  Broadly speaking, there are 3 parts to a network
     // definition.  The loss layer, a bunch of computational layers, and then an input
-    // layer.  You can see these components in the network definition below.  
-    // 
+    // layer.  You can see these components in the network definition below.
+    //
     // The input layer here says the network expects to be given matrix<unsigned char>
     // objects as input.  In general, you can use any dlib image or matrix type here, or
     // even define your own types by creating custom input layers.
@@ -59,29 +59,29 @@ int main(int argc, char** argv) try
     // Then the middle layers define the computation the network will do to transform the
     // input into whatever we want.  Here we run the image through multiple convolutions,
     // ReLU units, max pooling operations, and then finally a fully connected layer that
-    // converts the whole thing into just 10 numbers.  
-    // 
+    // converts the whole thing into just 10 numbers.
+    //
     // Finally, the loss layer defines the relationship between the network outputs, our 10
     // numbers, and the labels in our dataset.  Since we selected loss_multiclass_log it
     // means we want to do multiclass classification with our network.   Moreover, the
     // number of network outputs (i.e. 10) is the number of possible labels.  Whichever
     // network output is largest is the predicted label.  So for example, if the first
     // network output is largest then the predicted digit is 0, if the last network output
-    // is largest then the predicted digit is 9.  
+    // is largest then the predicted digit is 9.
     using net_type = loss_multiclass_log<
-                                fc<10,        
-                                relu<fc<84,   
-                                relu<fc<120,  
-                                max_pool<2,2,2,2,relu<con<16,5,5,1,1,
-                                max_pool<2,2,2,2,relu<con<6,5,5,1,1,
-                                input<matrix<unsigned char>> 
+                                fc<10,
+                                elu<fc<84,
+                                elu<fc<120,
+                                max_pool<2,2,2,2,elu<con<16,5,5,1,1,
+                                max_pool<2,2,2,2,elu<con<6,5,5,1,1,
+                                input<matrix<unsigned char>>
                                 >>>>>>>>>>>>;
     // This net_type defines the entire network architecture.  For example, the block
     // relu<fc<84,SUBNET>> means we take the output from the subnetwork, pass it through a
     // fully connected layer with 84 outputs, then apply ReLU.  Similarly, a block of
     // max_pool<2,2,2,2,relu<con<16,5,5,1,1,SUBNET>>> means we apply 16 convolutions with a
     // 5x5 filter size and 1x1 stride to the output of a subnetwork, then apply ReLU, then
-    // perform max pooling with a 2x2 window and 2x2 stride.  
+    // perform max pooling with a 2x2 window and 2x2 stride.
 
 
 
@@ -105,7 +105,7 @@ int main(int argc, char** argv) try
     // learning rate until the loss stops decreasing.  Then it reduces the learning rate by
     // a factor of 10 and continues running until the loss stops decreasing again.  It will
     // keep doing this until the learning rate has dropped below the min learning rate
-    // defined above or the maximum number of epochs as been executed (defaulted to 10000). 
+    // defined above or the maximum number of epochs as been executed (defaulted to 10000).
     trainer.train(training_images, training_labels);
 
     // At this point our net object should have learned how to classify MNIST images.  But
@@ -134,7 +134,7 @@ int main(int argc, char** argv) try
             ++num_right;
         else
             ++num_wrong;
-        
+
     }
     cout << "training num_right: " << num_right << endl;
     cout << "training num_wrong: " << num_wrong << endl;
@@ -151,7 +151,7 @@ int main(int argc, char** argv) try
             ++num_right;
         else
             ++num_wrong;
-        
+
     }
     cout << "testing num_right: " << num_right << endl;
     cout << "testing num_wrong: " << num_wrong << endl;
diff --git a/examples/dnn_mmod_train_find_cars_ex.cpp b/examples/dnn_mmod_train_find_cars_ex.cpp
index b97e25a85..227d78803 100644
--- a/examples/dnn_mmod_train_find_cars_ex.cpp
+++ b/examples/dnn_mmod_train_find_cars_ex.cpp
@@ -1,6 +1,6 @@
 // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
 /*
-    This example shows how to train a CNN based object detector using dlib's 
+    This example shows how to train a CNN based object detector using dlib's
     loss_mmod loss layer.  This loss layer implements the Max-Margin Object
     Detection loss as described in the paper:
         Max-Margin Object Detection by Davis E. King (http://arxiv.org/abs/1502.00046).
@@ -13,12 +13,12 @@
     example.  So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
     before reading this example program.  You should also read the introductory DNN+MMOD
     example dnn_mmod_ex.cpp as well before proceeding.
-    
+
 
     This example is essentially a more complex version of dnn_mmod_ex.cpp.  In it we train
     a detector that finds the rear ends of motor vehicles.  I will also discuss some
-    aspects of data preparation useful when training this kind of detector.  
-    
+    aspects of data preparation useful when training this kind of detector.
+
 */
 
 
@@ -35,7 +35,11 @@ template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2
 template <long num_filters, typename SUBNET> using con5  = con<num_filters,5,5,1,1,SUBNET>;
 template <typename SUBNET> using downsampler  = relu<bn_con<con5d<32, relu<bn_con<con5d<32, relu<bn_con<con5d<16,SUBNET>>>>>>>>>;
 template <typename SUBNET> using rcon5  = relu<bn_con<con5<55,SUBNET>>>;
-using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
+// using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
+// scale1<sig<con<55,1,1,1,1,avg_pool_everything<tag1<
+using net_type = loss_mmod<con<1,9,9,1,1,
+scale_prev2<skip1<tag2<sig<con<55,1,1,1,1,avg_pool_everything<tag1<
+rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>>>>>>>>;
 
 
 // ----------------------------------------------------------------------------------------
@@ -107,21 +111,21 @@ int main(int argc, char** argv) try
     // image is implicitly assumed to be not a car, and the algorithm will use it as
     // negative training data.  So every car must be labeled, either with a normal
     // rectangle or an "ignore" rectangle that tells MMOD to simply ignore it (i.e. neither
-    // treat it as a thing to detect nor as negative training data).  
-    // 
+    // treat it as a thing to detect nor as negative training data).
+    //
     // In our present case, many images contain very tiny cars in the distance, ones that
     // are essentially just dark smudges.  It's not reasonable to expect the CNN
     // architecture we defined to detect such vehicles.  However, I erred on the side of
     // having more complete annotations when creating the dataset.  So when I labeled these
-    // images I labeled many of these really difficult cases as vehicles to detect.   
+    // images I labeled many of these really difficult cases as vehicles to detect.
     //
     // So the first thing we are going to do is clean up our dataset a little bit.  In
     // particular, we are going to mark boxes smaller than 35*35 pixels as ignore since
     // only really small and blurry cars appear at those sizes.  We will also mark boxes
     // that are heavily overlapped by another box as ignore.  We do this because we want to
     // allow for stronger non-maximum suppression logic in the learned detector, since that
-    // will help make it easier to learn a good detector. 
-    // 
+    // will help make it easier to learn a good detector.
+    //
     // To explain this non-max suppression idea further it's important to understand how
     // the detector works.  Essentially, sliding window detectors scan all image locations
     // and ask "is there a car here?".  If there really is a car in a specific location in
@@ -143,7 +147,7 @@ int main(int argc, char** argv) try
     // "close to" measure will be configured to allow detections to really overlap a whole
     // lot.  On the other hand, if your dataset didn't contain any overlapped boxes at all,
     // then the non-max suppression logic would be configured to filter out any boxes that
-    // overlapped at all, and thus would be performing a much stronger non-max suppression.  
+    // overlapped at all, and thus would be performing a much stronger non-max suppression.
     //
     // Why does this matter?  Well, remember that we want to avoid duplicate detections.
     // If non-max suppression just kills everything in a really wide area around a car then
@@ -183,8 +187,8 @@ int main(int argc, char** argv) try
             // really extreme aspect ratios.  However, some datasets do, often because of
             // bad labeling.  So it's a good idea to check for that and either eliminate
             // those boxes or set them to ignore.  Although, this depends on your
-            // application.  
-            // 
+            // application.
+            //
             // For instance, if your dataset has boxes with an aspect ratio
             // of 10 then you should think about what that means for the network
             // architecture.  Does the receptive field even cover the entirety of the box
@@ -196,13 +200,13 @@ int main(int argc, char** argv) try
             // errors, but are annotated in a sloppy and inconsistent way.  Fixing those
             // errors and inconsistencies can often greatly improve models trained from
             // such data.  It's almost always worth the time to try and improve your
-            // training dataset.   
+            // training dataset.
             //
             // In any case, my point is that there are other types of dataset cleaning you
             // could put here.  What exactly you need depends on your application.  But you
             // should carefully consider it and not take your dataset as a given.  The work
             // of creating a good detector is largely about creating a high quality
-            // training dataset.  
+            // training dataset.
         }
     }
 
@@ -226,7 +230,7 @@ int main(int argc, char** argv) try
     // each of the sliding windows needs to be so as to be able to detect all the vehicles.
     // Since our dataset has basically these 3 different aspect ratios, it will decide to
     // use 3 different sliding windows.  This means the final con layer in the network will
-    // have 3 filters, one for each of these aspect ratios. 
+    // have 3 filters, one for each of these aspect ratios.
     //
     // Another thing to consider when setting the sliding window size is the "stride" of
     // your network.  The network we defined above downsamples the image by a factor of 8x
@@ -237,7 +241,7 @@ int main(int argc, char** argv) try
     // pixels at a time when scanning. This is obviously a problem since 75% of the image
     // won't even be visited by the sliding window.  So you need to set the window size to
     // be big enough relative to the stride of your network.  In our case, the windows are
-    // at least 30 pixels in length, so being moved by 8 pixel steps is fine. 
+    // at least 30 pixels in length, so being moved by 8 pixel steps is fine.
     mmod_options options(boxes_train, 70, 30);
 
 
@@ -247,22 +251,22 @@ int main(int argc, char** argv) try
     // also contained a lot of ignore boxes.  Some of them are large boxes that encompass
     // large parts of an image and the intention is to have everything inside those boxes
     // be ignored.  Therefore, we need to tell the MMOD algorithm to do that, which we do
-    // by setting options.overlaps_ignore appropriately.  
-    // 
+    // by setting options.overlaps_ignore appropriately.
+    //
     // But first, we need to understand exactly what this option does.  The MMOD loss
     // is essentially counting the number of false alarms + missed detections produced by
     // the detector for each image.  During training, the code is running the detector on
     // each image in a mini-batch and looking at its output and counting the number of
     // mistakes.  The optimizer tries to find parameters settings that minimize the number
     // of detector mistakes.
-    // 
+    //
     // This overlaps_ignore option allows you to tell the loss that some outputs from the
     // detector should be totally ignored, as if they never happened.  In particular, if a
     // detection overlaps a box in the training data with ignore==true then that detection
     // is ignored.  This overlap is determined by calling
     // options.overlaps_ignore(the_detection, the_ignored_training_box).  If it returns
     // true then that detection is ignored.
-    // 
+    //
     // You should read the documentation for test_box_overlap, the class type for
     // overlaps_ignore for full details.  However, the gist is that the default behavior is
     // to only consider boxes as overlapping if their intersection over union is > 0.5.
@@ -275,7 +279,7 @@ int main(int argc, char** argv) try
 
     net_type net(options);
 
-    // The final layer of the network must be a con layer that contains 
+    // The final layer of the network must be a con layer that contains
     // options.detector_windows.size() filters.  This is because these final filters are
     // what perform the final "sliding window" detection in the network.  For the dlib
     // vehicle dataset, there will be 3 sliding window detectors, so we will be setting
@@ -306,13 +310,13 @@ int main(int argc, char** argv) try
 
 
     std::vector<matrix<rgb_pixel>> mini_batch_samples;
-    std::vector<std::vector<mmod_rect>> mini_batch_labels; 
+    std::vector<std::vector<mmod_rect>> mini_batch_labels;
     random_cropper cropper;
     cropper.set_seed(time(0));
     cropper.set_chip_dims(350, 350);
     // Usually you want to give the cropper whatever min sizes you passed to the
     // mmod_options constructor, or very slightly smaller sizes, which is what we do here.
-    cropper.set_min_object_size(69,28); 
+    cropper.set_min_object_size(69,28);
     cropper.set_max_rotation_degrees(2);
     dlib::rand rnd;
 
@@ -320,10 +324,10 @@ int main(int argc, char** argv) try
     cout << trainer << cropper << endl;
 
     int cnt = 1;
-    // Run the trainer until the learning rate gets small.  
+    // Run the trainer until the learning rate gets small.
     while(trainer.get_learning_rate() >= 1e-4)
     {
-        // Every 30 mini-batches we do a testing mini-batch.  
+        // Every 30 mini-batches we do a testing mini-batch.
         if (cnt%30 != 0 || images_test.size() == 0)
         {
             cropper(87, images_train, boxes_train, mini_batch_samples, mini_batch_labels);
@@ -375,7 +379,7 @@ int main(int argc, char** argv) try
     cout << "\nsync_filename: " << sync_filename << endl;
     cout << "num training images: "<< images_train.size() << endl;
     cout << "training results: " << test_object_detection_function(net, images_train, boxes_train, test_box_overlap(), 0, options.overlaps_ignore);
-    // Upsampling the data will allow the detector to find smaller cars.  Recall that 
+    // Upsampling the data will allow the detector to find smaller cars.  Recall that
     // we configured it to use a sliding window nominally 70 pixels in size.  So upsampling
     // here will let it find things nominally 35 pixels in size.  Although we include a
     // limit of 1800*1800 here which means "don't upsample an image if it's already larger
@@ -405,11 +409,11 @@ int main(int argc, char** argv) try
 
         Also, the training and testing accuracies were:
             num training images: 2217
-            training results: 0.990738 0.736431 0.736073 
-            training upsampled results: 0.986837 0.937694 0.936912 
+            training results: 0.990738 0.736431 0.736073
+            training upsampled results: 0.986837 0.937694 0.936912
             num testing images: 135
-            testing results: 0.988827 0.471372 0.470806 
-            testing upsampled results: 0.987879 0.651132 0.650399 
+            testing results: 0.988827 0.471372 0.470806
+            testing upsampled results: 0.987879 0.651132 0.650399
     */
 
     return 0;