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Is it time to release 1.0 version?
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blacklight
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140 changed files with 1710 additions and 829 deletions
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@ -1,10 +1,11 @@
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all:
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g++ -Wall -o learnAdd learnAdd.cpp -lneural++
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g++ -Wall -o doAdd doAdd.cpp -lneural++
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g++ -Wall -o adderFromScratch adderFromScratch.cpp -lneural++
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g++ -Wall -o Add Add.cpp -lneural++
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g++ -Wall -o networkForSumsAndSubtractions networkForSumsAndSubtractions.cpp -lneural++
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g++ -Wall -o adderFromString adderFromString.cpp -lneural++
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clean:
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rm learnAdd
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rm doAdd
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rm adderFromScratch
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rm networkForSumsAndSubtractions
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rm adderFromString
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@ -1,19 +0,0 @@
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* @example examples/learnAdd.cpp 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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* @example examples/doAdd.cpp Show how to use a network already trained and saved to a
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* binary file. In this case, a network trained to simply perform sums between two real
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* numbers, that should have already been created using learnAdd.
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* @example examples/adderFromScratch.cpp Similar to learnAdd.cpp, but this time the
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* training XML is generated as a string and not saved to a file, and parsed by the
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* program itself to build the network. Then, the program asks two real numbers, and
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* performs both the sum and the difference between them, putting the sum's output on
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* the first output neuron and the difference's on the second output neuron. Anyway,
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* using more than one neuron in the output layer is strongly discouraged, as the network
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* usually won't set correctly the synaptical weights to give satisfying and accurate
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* answers for all of the operations.
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91
examples/adder.xml
Normal file
91
examples/adder.xml
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@ -0,0 +1,91 @@
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<?xml version="1.0" encoding="iso-8859-1"?>
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<!DOCTYPE NETWORK SYSTEM "http://blacklight.gotdns.org/prog/neuralpp/trainer.dtd">
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<!-- Automatically generated by Neural++ library - by BlackLight -->
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<network>
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<training id="0">
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<input id="1">2</input>
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<input id="2">3</input>
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<output id="3">5</output>
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</training>
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<training id="4">
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<input id="5">3</input>
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<input id="6">2</input>
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<output id="7">5</output>
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</training>
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<training id="8">
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<input id="9">6</input>
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<input id="10">2</input>
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<output id="11">8</output>
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</training>
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<training id="12">
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<input id="13">2</input>
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<input id="14">2</input>
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<output id="15">4</output>
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</training>
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<training id="16">
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<input id="17">1</input>
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<input id="18">2</input>
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<output id="19">3</output>
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</training>
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<training id="20">
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<input id="21">-1</input>
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<input id="22">-2</input>
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<output id="23">-3</output>
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</training>
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<training id="24">
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<input id="25">8</input>
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<input id="26">9</input>
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<output id="27">17</output>
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</training>
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<training id="28">
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<input id="29">10</input>
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<input id="30">10</input>
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<output id="31">20</output>
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</training>
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<training id="32">
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<input id="33">4</input>
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<input id="34">1</input>
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<output id="35">5</output>
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</training>
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<training id="36">
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<input id="37">2</input>
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<input id="38">6</input>
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<output id="39">8</output>
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</training>
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<training id="40">
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<input id="41">2</input>
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<input id="42">7</input>
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<output id="43">9</output>
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</training>
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<training id="44">
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<input id="45">8</input>
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<input id="46">9</input>
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<output id="47">17</output>
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</training>
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<training id="48">
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<input id="49">4</input>
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<input id="50">7</input>
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<output id="51">11</output>
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</training>
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<training id="52">
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<input id="53">5</input>
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<input id="54">2</input>
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<output id="55">7</output>
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</training>
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</network>
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@ -18,7 +18,7 @@ using namespace std;
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using namespace neuralpp;
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int main() {
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NeuralNet net(2, 2, 2, 0.005, 100);
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NeuralNet net(2, 2, 1, 0.002, 1000);
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string xml;
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double tmp;
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int id = 0;
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@ -26,9 +26,10 @@ int main() {
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// XML initialization. Then, I say XML that 2+3=5, 3+3=6, 5+4=9
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// Strings' format is "input1,input2,...,inputn;output1,output2,...,outputm
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NeuralNet::initXML(xml);
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xml += NeuralNet::XMLFromSet(id, "3,2;5,1");
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xml += NeuralNet::XMLFromSet(id, "4,2;6,2");
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xml += NeuralNet::XMLFromSet(id, "6,3;9,3");
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xml += NeuralNet::XMLFromSet(id, "3,2;5");
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xml += NeuralNet::XMLFromSet(id, "6,3;9");
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xml += NeuralNet::XMLFromSet(id, "2,3;5");
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xml += NeuralNet::XMLFromSet(id, "4,4;8");
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NeuralNet::closeXML(xml);
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cout << xml << endl;
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net.setInput(v);
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net.propagate();
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cout << "Output: " << net.getOutputs()[0] << "; " << net.getOutputs()[1] << endl;
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cout << "Output: " << net.getOutput() << endl;
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return 0;
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}
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@ -49,6 +49,8 @@ int main() {
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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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xml += NeuralNet::XMLFromSet(id, "4,7;11");
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xml += NeuralNet::XMLFromSet(id, "5,2;7");
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NeuralNet::closeXML(xml);
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// Save the XML string just created to a file
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16
examples/network.xml
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16
examples/network.xml
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<?xml version="1.0" encoding="iso-8859-1"?>
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<!DOCTYPE NETWORK SYSTEM "http://blacklight.gotdns.org/prog/neuralpp/network.dtd">
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<!-- Automatically generated by BlackLight's Neural++ module -->
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<network name="Put here the name for this neural network" epochs="2000" learning_rate="0.002" threshold="0">
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<layer class="input" size="2"></layer>
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<layer class="hidden" size="2"></layer>
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<layer class="output" size="1"></layer>
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<synapsis class="inhid" input="0" output="0" weight="0.681455"></synapsis>
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<synapsis class="inhid" input="1" output="0" weight="0.770994"></synapsis>
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<synapsis class="inhid" input="0" output="1" weight="0.656911"></synapsis>
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<synapsis class="inhid" input="1" output="1" weight="0.763797"></synapsis>
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<synapsis class="hidout" input="0" output="0" weight="0.751936"></synapsis>
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<synapsis class="hidout" input="1" output="0" weight="0.68129"></synapsis>
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</network>
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48
examples/networkForSumsAndSubtractions.cpp
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48
examples/networkForSumsAndSubtractions.cpp
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/**
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* This program creates a neural network from scratch. Its purpose is to get
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* two numbers and learn to compute their sum and difference (so the network
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* provides two output values). The training set is auto-generated to an XML
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* string, and then the network is trained.
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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 <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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NeuralNet net(2, 2, 2, 0.002, 1000);
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string xml;
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double tmp;
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int id = 0;
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// XML initialization. Then, I say XML that 3+2=5, 3-2=1; 4+2=6, 4-2=2; 6+3=9, 6-3=3
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// Strings' format is "input1,input2,...,inputn;output1,output2,...,outputm
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NeuralNet::initXML(xml);
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xml += NeuralNet::XMLFromSet(id, "3,2;5,1");
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xml += NeuralNet::XMLFromSet(id, "4,2;6,2");
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xml += NeuralNet::XMLFromSet(id, "6,3;9,3");
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NeuralNet::closeXML(xml);
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cout << xml << endl;
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net.train(xml, NeuralNet::str);
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vector<double> v;
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cout << "Network status: trained\n\n";
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cout << "First number: ";
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cin >> tmp;
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v.push_back(tmp);
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cout << "Second number: ";
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cin >> tmp;
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v.push_back(tmp);
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net.setInput(v);
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net.propagate();
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cout << "Output: " << net.getOutputs()[0] << "; " << net.getOutputs()[1] << endl;
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return 0;
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}
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