Added metric learning example

examples/CMakeLists.txt

@@ -46,6 +46,7 @@ if (NOT USING_OLD_VISUAL_STUDIO_COMPILER)
    add_example(dnn_introduction_ex)
    add_example(dnn_introduction2_ex)
    add_example(dnn_inception_ex)
+   add_example(dnn_metric_learning_ex)
    add_gui_example(dnn_imagenet_ex)
    add_gui_example(dnn_mmod_ex)
    add_gui_example(dnn_mmod_face_detection_ex)

examples/dnn_metric_learning_ex.cpp

@@ -0,0 +1,88 @@
+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    This is an example illustrating the use of the deep learning tools from the
+    dlib C++ Library.  In it, we will show how to use the loss_metric layer to do
+    metric learning.  
+
+
+*/
+
+
+#include <dlib/dnn.h>
+#include <iostream>
+
+using namespace std;
+using namespace dlib;
+
+
+int main() try
+{
+    using net_type = loss_metric<fc<2,input<matrix<double,0,1>>>>; 
+
+    net_type net;
+    dnn_trainer<net_type> trainer(net);
+    trainer.set_learning_rate(0.1);
+    trainer.set_min_learning_rate(0.00001);
+    trainer.set_mini_batch_size(128);
+    trainer.be_verbose();
+    trainer.set_iterations_without_progress_threshold(100);
+
+
+
+    std::vector<matrix<double,0,1>> samples;
+    std::vector<unsigned long> labels;
+
+    samples.push_back({1,0,0,0,0,0,0,0}); labels.push_back(1);
+    samples.push_back({0,1,0,0,0,0,0,0}); labels.push_back(1);
+
+    samples.push_back({0,0,1,0,0,0,0,0}); labels.push_back(2);
+    samples.push_back({0,0,0,1,0,0,0,0}); labels.push_back(2);
+
+    samples.push_back({0,0,0,0,1,0,0,0}); labels.push_back(3);
+    samples.push_back({0,0,0,0,0,1,0,0}); labels.push_back(3);
+
+    samples.push_back({0,0,0,0,0,0,1,0}); labels.push_back(4);
+    samples.push_back({0,0,0,0,0,0,0,1}); labels.push_back(4);
+
+    trainer.train(samples, labels);
+
+
+    auto embedded = net(samples);
+
+    for (size_t i = 0; i < embedded.size(); ++i)
+        cout << "label: " << labels[i] << "\t" << trans(embedded[i]);
+
+    // now count how many pairs are correctly classified.
+    int num_right = 0;
+    int num_wrong = 0;
+    for (size_t i = 0; i < embedded.size(); ++i)
+    {
+        for (size_t j = i+1; j < embedded.size(); ++j)
+        {
+            if (labels[i] == labels[j])
+            {
+                if (length(embedded[i]-embedded[j]) < net.loss_details().get_distance_threshold())
+                    ++num_right;
+                else
+                    ++num_wrong;
+            }
+            else
+            {
+                if (length(embedded[i]-embedded[j]) < net.loss_details().get_distance_threshold())
+                    ++num_wrong;
+                else
+                    ++num_right;
+            }
+        }
+    }
+
+    cout << "num_right: "<< num_right << endl;
+    cout << "num_wrong: "<< num_wrong << endl;
+
+
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+