dlib C++ Library - mlp_ex.cpp

// 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 multilayer perceptron 
 from the dlib C++ Library. 
 This example creates a simple set of data to train on and shows
 you how to train a mlp object on that data.
 The data used in this example will be 2 dimensional data and will
 come from a distribution where points with a distance less than 10
 from the origin are labeled 1 and all other points are labeled
 as 0.
 
*/
#include <iostream>
#include <dlib/mlp.h>
using namespace std;
using namespace dlib;
int main()
{
 // The mlp takes column vectors as input and gives column vectors as output. The dlib::matrix
 // object is used to represent the column vectors. So the first thing we do here is declare 
 // a convenient typedef for the matrix object we will be using.
 // This typedef declares a matrix with 2 rows and 1 column. It will be the
 // object that contains each of our 2 dimensional samples. (Note that if you wanted 
 // more than 2 features in this vector you can simply change the 2 to something else)
 typedef matrix<double, 2, 1> sample_type;
 // make an instance of a sample matrix so we can use it below
 sample_type sample;
 // Create a multi-layer perceptron network. This network has 2 nodes on the input layer 
 // (which means it takes column vectors of length 2 as input) and 5 nodes in the first 
 // hidden layer. Note that the other 4 variables in the mlp's constructor are left at
 // their default values. 
 mlp::kernel_1a_c net(2,5);
 // Now let's put some data into our sample and train on it. We do this
 // by looping over 41*41 points and labeling them according to their
 // distance from the origin.
 for (int i = 0; i < 1000; ++i)
 {
 for (int r = -20; r <= 20; ++r)
 {
 for (int c = -20; c <= 20; ++c)
 {
 sample(0) = r;
 sample(1) = c;
 // if this point is less than 10 from the origin
 if (sqrt((double)r*r + c*c) <= 10)
 net.train(sample,1);
 else
 net.train(sample,0);
 }
 }
 }
 // Now we have trained our mlp. Let's see how well it did. 
 // Note that if you run this program multiple times you will get different results. This
 // is because the mlp network is randomly initialized.
 // each of these statements prints out the output of the network given a particular sample.
 sample(0) = 3.123;
 sample(1) = 4;
 cout << "This sample should be close to 1 and it is classified as a " << net(sample) << endl;
 sample(0) = 13.123;
 sample(1) = 9.3545;
 cout << "This sample should be close to 0 and it is classified as a " << net(sample) << endl;
 sample(0) = 13.123;
 sample(1) = 0;
 cout << "This sample should be close to 0 and it is classified as a " << net(sample) << endl;
}