73 lines
2.6 KiB
C++
73 lines
2.6 KiB
C++
/**
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* Show how to train a network that performs sums between
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* two real numbers. The training XML is built from scratch, then saved to a file, then
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* the network is initialized using that XML file, trained, and the resulting trained
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* network is saved to adder.net. Then, you should take a look at doAdd.cpp to see how
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* to use that file to use the network.
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*
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* by BlackLight, 2009
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*/
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#include <iostream>
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#include <fstream>
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#include <ctime>
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#include <neural++.hpp>
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using namespace std;
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using namespace neuralpp;
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int main() {
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int id = 0;
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string xml;
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time_t t1, t2;
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// Create the neural network. The network is going to have
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// => 2 neurons for the input layer
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// => 2 neurons for the hidden layer
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// => 1 neuron for the output layer
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// => a learning rate == 0.002 (just get it doing some tests until satisfied, but remember to keep its value quite low and ~ 0 to keep the network stable)
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// => 1000 learning steps (i.e. the network will be ready after 1000 training steps to adjust the synaptical weights
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// => 0.1 as neural threshold (the threshold above which a neuron activates)
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NeuralNet net(2, 2, 1, 0.002, 2000);
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// Initialize a training XML as a string in 'xml'
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NeuralNet::initXML(xml);
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// Build some training sets for the XML. The format is:
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// "input1,input2,...,inputn;output1,output2,...,outputn
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// The 'id' variable is passed as reference, starting from 0,
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// and it's used to enumerate the sets in the XML file.
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xml += NeuralNet::XMLFromSet(id, "2,3;5");
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xml += NeuralNet::XMLFromSet(id, "3,2;5");
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xml += NeuralNet::XMLFromSet(id, "6,2;8");
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xml += NeuralNet::XMLFromSet(id, "2,2;4");
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xml += NeuralNet::XMLFromSet(id, "1,2;3");
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xml += NeuralNet::XMLFromSet(id, "-1,-2;-3");
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xml += NeuralNet::XMLFromSet(id, "8,9;17");
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xml += NeuralNet::XMLFromSet(id, "10,10;20");
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xml += NeuralNet::XMLFromSet(id, "4,1;5");
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xml += NeuralNet::XMLFromSet(id, "2,6;8");
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xml += NeuralNet::XMLFromSet(id, "2,7;9");
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xml += NeuralNet::XMLFromSet(id, "8,9;17");
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NeuralNet::closeXML(xml);
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// Save the XML string just created to a file
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ofstream out("adder.xml");
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out << xml;
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out.close();
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cout << "Training file adder.xml has been written\n";
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// Start the training from the XML file
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t1 = time(NULL);
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cout << "Training in progress - This may take a while...\n";
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net.train("adder.xml", NeuralNet::file);
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t2 = time(NULL);
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// Save the trained network to a binary file, that can be reloaded from any
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// application that is going to use that network
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net.save("network.xml");
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cout << "Network trained in " << (t2-t1) << " seconds. You can use adder.net file now to load this network\n";
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return 0;
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}
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