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<html><!-- Created using the cpp_pretty_printer from the dlib C++ library. See http://dlib.net for updates. --><head><title>dlib C++ Library - dnn_metric_learning_ex.cpp</title></head><body bgcolor='white'><pre>
<font color='#009900'>// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
</font><font color='#009900'>/*
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.
The main reason you might want to use this kind of algorithm is because you
would like to use a k-nearest neighbor classifier or similar algorithm, but
you don't know a good way to calculate the distance between two things. A
popular example would be face recognition. There are a whole lot of papers
that train some kind of deep metric learning algorithm that embeds face
images in some vector space where images of the same person are close to each
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
from 8D vectors to 2D vectors such that vectors with the same class labels
are near each other. If you want to see a more complex example that learns
the kind of network you would use for something like face recognition read
the <a href="dnn_metric_learning_on_images_ex.cpp.html">dnn_metric_learning_on_images_ex.cpp</a> example.
You should also have read the examples that introduce the dlib DNN API before
continuing. These are <a href="dnn_introduction_ex.cpp.html">dnn_introduction_ex.cpp</a> and <a href="dnn_introduction2_ex.cpp.html">dnn_introduction2_ex.cpp</a>.
*/</font>
<font color='#0000FF'>#include</font> <font color='#5555FF'>&lt;</font>dlib<font color='#5555FF'>/</font>dnn.h<font color='#5555FF'>&gt;</font>
<font color='#0000FF'>#include</font> <font color='#5555FF'>&lt;</font>iostream<font color='#5555FF'>&gt;</font>
<font color='#0000FF'>using</font> <font color='#0000FF'>namespace</font> std;
<font color='#0000FF'>using</font> <font color='#0000FF'>namespace</font> dlib;
<font color='#0000FF'><u>int</u></font> <b><a name='main'></a>main</b><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font> <font color='#0000FF'>try</font>
<b>{</b>
<font color='#009900'>// The API for doing metric learning is very similar to the API for
</font> <font color='#009900'>// multi-class classification. In fact, the inputs are the same, a bunch of
</font> <font color='#009900'>// labeled objects. So here we create our dataset. We make up some simple
</font> <font color='#009900'>// vectors and label them with the integers 1,2,3,4. The specific values of
</font> <font color='#009900'>// the integer labels don't matter.
</font> std::vector<font color='#5555FF'>&lt;</font>matrix<font color='#5555FF'>&lt;</font><font color='#0000FF'><u>double</u></font>,<font color='#979000'>0</font>,<font color='#979000'>1</font><font color='#5555FF'>&gt;</font><font color='#5555FF'>&gt;</font> samples;
std::vector<font color='#5555FF'>&lt;</font><font color='#0000FF'><u>unsigned</u></font> <font color='#0000FF'><u>long</u></font><font color='#5555FF'>&gt;</font> labels;
<font color='#009900'>// class 1 training vectors
</font> samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>1</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>1</font><font face='Lucida Console'>)</font>;
samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>0</font>,<font color='#979000'>1</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>1</font><font face='Lucida Console'>)</font>;
<font color='#009900'>// class 2 training vectors
</font> samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>1</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>2</font><font face='Lucida Console'>)</font>;
samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>1</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>2</font><font face='Lucida Console'>)</font>;
<font color='#009900'>// class 3 training vectors
</font> samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>1</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>3</font><font face='Lucida Console'>)</font>;
samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>1</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>3</font><font face='Lucida Console'>)</font>;
<font color='#009900'>// class 4 training vectors
</font> samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>1</font>,<font color='#979000'>0</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>4</font><font face='Lucida Console'>)</font>;
samples.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><b>{</b><font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>0</font>,<font color='#979000'>1</font><b>}</b><font face='Lucida Console'>)</font>; labels.<font color='#BB00BB'>push_back</font><font face='Lucida Console'>(</font><font color='#979000'>4</font><font face='Lucida Console'>)</font>;
<font color='#009900'>// Make a network that simply learns a linear mapping from 8D vectors to 2D
</font> <font color='#009900'>// vectors.
</font> <font color='#0000FF'>using</font> net_type <font color='#5555FF'>=</font> loss_metric<font color='#5555FF'>&lt;</font>fc<font color='#5555FF'>&lt;</font><font color='#979000'>2</font>,input<font color='#5555FF'>&lt;</font>matrix<font color='#5555FF'>&lt;</font><font color='#0000FF'><u>double</u></font>,<font color='#979000'>0</font>,<font color='#979000'>1</font><font color='#5555FF'>&gt;</font><font color='#5555FF'>&gt;</font><font color='#5555FF'>&gt;</font><font color='#5555FF'>&gt;</font>;
net_type net;
dnn_trainer<font color='#5555FF'>&lt;</font>net_type<font color='#5555FF'>&gt;</font> <font color='#BB00BB'>trainer</font><font face='Lucida Console'>(</font>net<font face='Lucida Console'>)</font>;
trainer.<font color='#BB00BB'>set_learning_rate</font><font face='Lucida Console'>(</font><font color='#979000'>0.1</font><font face='Lucida Console'>)</font>;
<font color='#009900'>// It should be emphasized out that it's really important that each mini-batch contain
</font> <font color='#009900'>// multiple instances of each class of object. This is because the metric learning
</font> <font color='#009900'>// algorithm needs to consider pairs of objects that should be close as well as pairs
</font> <font color='#009900'>// of objects that should be far apart during each training step. Here we just keep
</font> <font color='#009900'>// training on the same small batch so this constraint is trivially satisfied.
</font> <font color='#0000FF'>while</font><font face='Lucida Console'>(</font>trainer.<font color='#BB00BB'>get_learning_rate</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font> <font color='#5555FF'>&gt;</font><font color='#5555FF'>=</font> <font color='#979000'>1e</font><font color='#5555FF'>-</font><font color='#979000'>4</font><font face='Lucida Console'>)</font>
trainer.<font color='#BB00BB'>train_one_step</font><font face='Lucida Console'>(</font>samples, labels<font face='Lucida Console'>)</font>;
<font color='#009900'>// Wait for training threads to stop
</font> trainer.<font color='#BB00BB'>get_net</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>;
cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>done training</font>" <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
<font color='#009900'>// Run all the samples through the network to get their 2D vector embeddings.
</font> std::vector<font color='#5555FF'>&lt;</font>matrix<font color='#5555FF'>&lt;</font><font color='#0000FF'><u>float</u></font>,<font color='#979000'>0</font>,<font color='#979000'>1</font><font color='#5555FF'>&gt;</font><font color='#5555FF'>&gt;</font> embedded <font color='#5555FF'>=</font> <font color='#BB00BB'>net</font><font face='Lucida Console'>(</font>samples<font face='Lucida Console'>)</font>;
<font color='#009900'>// Print the embedding for each sample to the screen. If you look at the
</font> <font color='#009900'>// outputs carefully you should notice that they are grouped together in 2D
</font> <font color='#009900'>// space according to their label.
</font> <font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>size_t</u></font> i <font color='#5555FF'>=</font> <font color='#979000'>0</font>; i <font color='#5555FF'>&lt;</font> embedded.<font color='#BB00BB'>size</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>i<font face='Lucida Console'>)</font>
cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>label: </font>" <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> labels[i] <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>\t</font>" <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> <font color='#BB00BB'>trans</font><font face='Lucida Console'>(</font>embedded[i]<font face='Lucida Console'>)</font>;
<font color='#009900'>// Now, check if the embedding puts things with the same labels near each other and
</font> <font color='#009900'>// things with different labels far apart.
</font> <font color='#0000FF'><u>int</u></font> num_right <font color='#5555FF'>=</font> <font color='#979000'>0</font>;
<font color='#0000FF'><u>int</u></font> num_wrong <font color='#5555FF'>=</font> <font color='#979000'>0</font>;
<font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>size_t</u></font> i <font color='#5555FF'>=</font> <font color='#979000'>0</font>; i <font color='#5555FF'>&lt;</font> embedded.<font color='#BB00BB'>size</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>i<font face='Lucida Console'>)</font>
<b>{</b>
<font color='#0000FF'>for</font> <font face='Lucida Console'>(</font><font color='#0000FF'><u>size_t</u></font> j <font color='#5555FF'>=</font> i<font color='#5555FF'>+</font><font color='#979000'>1</font>; j <font color='#5555FF'>&lt;</font> embedded.<font color='#BB00BB'>size</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>; <font color='#5555FF'>+</font><font color='#5555FF'>+</font>j<font face='Lucida Console'>)</font>
<b>{</b>
<font color='#0000FF'>if</font> <font face='Lucida Console'>(</font>labels[i] <font color='#5555FF'>=</font><font color='#5555FF'>=</font> labels[j]<font face='Lucida Console'>)</font>
<b>{</b>
<font color='#009900'>// The loss_metric layer will cause things with the same label to be less
</font> <font color='#009900'>// than net.loss_details().get_distance_threshold() distance from each
</font> <font color='#009900'>// other. So we can use that distance value as our testing threshold for
</font> <font color='#009900'>// "being near to each other".
</font> <font color='#0000FF'>if</font> <font face='Lucida Console'>(</font><font color='#BB00BB'>length</font><font face='Lucida Console'>(</font>embedded[i]<font color='#5555FF'>-</font>embedded[j]<font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font> net.<font color='#BB00BB'>loss_details</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>.<font color='#BB00BB'>get_distance_threshold</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>
<font color='#5555FF'>+</font><font color='#5555FF'>+</font>num_right;
<font color='#0000FF'>else</font>
<font color='#5555FF'>+</font><font color='#5555FF'>+</font>num_wrong;
<b>}</b>
<font color='#0000FF'>else</font>
<b>{</b>
<font color='#0000FF'>if</font> <font face='Lucida Console'>(</font><font color='#BB00BB'>length</font><font face='Lucida Console'>(</font>embedded[i]<font color='#5555FF'>-</font>embedded[j]<font face='Lucida Console'>)</font> <font color='#5555FF'>&gt;</font><font color='#5555FF'>=</font> net.<font color='#BB00BB'>loss_details</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font>.<font color='#BB00BB'>get_distance_threshold</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font><font face='Lucida Console'>)</font>
<font color='#5555FF'>+</font><font color='#5555FF'>+</font>num_right;
<font color='#0000FF'>else</font>
<font color='#5555FF'>+</font><font color='#5555FF'>+</font>num_wrong;
<b>}</b>
<b>}</b>
<b>}</b>
cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>num_right: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> num_right <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> "<font color='#CC0000'>num_wrong: </font>"<font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> num_wrong <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
<b>}</b>
<font color='#0000FF'>catch</font><font face='Lucida Console'>(</font>std::exception<font color='#5555FF'>&amp;</font> e<font face='Lucida Console'>)</font>
<b>{</b>
cout <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> e.<font color='#BB00BB'>what</font><font face='Lucida Console'>(</font><font face='Lucida Console'>)</font> <font color='#5555FF'>&lt;</font><font color='#5555FF'>&lt;</font> endl;
<b>}</b>
</pre></body></html>