// 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;intmain(){// 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 (inti = 0; i < 1000; ++i){for (intr = -20; r <= 20; ++r){for (intc = -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;}