(C++) Example 2: 'friendly' neural net getting ready to tackle the XOR problem

 

Shark example 2: 'friendly' neural net getting ready to tackle the XOR problem is a Shark example to let a neural network train to solve the XOR problem.

 

• Download the Qt Creator project 'CppSharkExample2' (zip)

 

 

 

 

 

Operating system: Ubuntu 10.04 LTS Lucid Lynx

IDE: Qt Creator 2.0.0

Project type: GUI application

Compiler: G++ 4.4.1

Libraries used:

• Shark: version 2.3.2

 

 

 

 

 

Qt project file

 

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#------------------------------------------------- # # Project created by QtCreator 2010-08-15T23:08:25 # #------------------------------------------------- QT += core QT -= gui TARGET = CppSharkExample2 CONFIG += console CONFIG -= app_bundle LIBS += -L/usr/local/lib -lshark TEMPLATE = app SOURCES += main.cpp

 

 

 

 

 

main.cpp

 

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#include <cassert> #include <iostream> #include <iterator> #include <Array/Array.h> #include <ReClaM/FFNet.h> #include <ReClaM/createConnectionMatrix.h> ///NeuralNet is a derived class of FFNet ///to gain access to some protected methods of FFNet struct NeuralNet : public FFNet {   NeuralNet(     const int n_inputs,     const int n_outputs,     const Array<int> connection_matrix)   : FFNet( n_inputs,n_outputs,connection_matrix) {}   void Activate(const Array<double> &in)   {     this->activate(in);   }   unsigned int GetNumberOfNeurons()   {     return this->numberOfNeurons;   } }; NeuralNet CreateNet(   const int n_inputs,   const int n_hidden,   const int n_outputs,   const double init_weight_min,   const double init_weight_max) {   //Create neural net connection matrix   Array<int> connection_matrix;   createConnectionMatrix(connection_matrix,n_inputs, n_hidden, n_outputs);   //Create the feed-forward neural network   NeuralNet n(n_inputs, n_outputs, connection_matrix);   n.initWeights(init_weight_min,init_weight_max);   return n; } void ShowXor(NeuralNet& n) {   std::cout << "Show how the neural net responds to input:\n";   const unsigned int output_neuron_index = n.GetNumberOfNeurons() - 1;   {     std::vector<double> v(2);     v[0] = 0.0;     v[1] = 0.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     std::cout << "(" << v[0] << "," << v[1] << "): " << output       << " (and should get to " << (v[0]!=v[1] ? "1.0" : "0.0") <<  ")'\n";   }   {     std::vector<double> v(2);     v[0] = 1.0;     v[1] = 0.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     std::cout << "(" << v[0] << "," << v[1] << "): " << output       << " (and should get to " << (v[0]!=v[1] ? "1.0" : "0.0") <<  ")'\n";   }   {     std::vector<double> v(2);     v[0] = 0.0;     v[1] = 1.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     std::cout << "(" << v[0] << "," << v[1] << "): " << output       << " (and should get to " << (v[0]!=v[1] ? "1.0" : "0.0") <<  ")'\n";   }   {     std::vector<double> v(2);     v[0] = 1.0;     v[1] = 1.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     std::cout << "(" << v[0] << "," << v[1] << "): " << output       << " (and should get to " << (v[0]!=v[1] ? "1.0" : "0.0") <<  ")'\n";   } } double Rate_xor_success(NeuralNet& n) {   double rating = 4.0;   const unsigned int output_neuron_index = n.GetNumberOfNeurons() - 1;   {     std::vector<double> v(2);     v[0] = 0.0;     v[1] = 0.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     rating -= std::fabs(0.0 - output);   }   {     std::vector<double> v(2);     v[0] = 1.0;     v[1] = 0.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     rating -= std::fabs(1.0 - output);   }   {     std::vector<double> v(2);     v[0] = 0.0;     v[1] = 1.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     rating -= std::fabs(1.0 - output);   }   {     std::vector<double> v(2);     v[0] = 1.0;     v[1] = 1.0;     Array<double> inputs(v);     n.Activate(inputs);     const double output = n.outputValue(output_neuron_index);     rating -= std::fabs(0.0 - output);   }   return rating; } int main() {   NeuralNet net = CreateNet(2,2,1,-0.1,0.1);   const double success = Rate_xor_success(net);   ShowXor(net);   std::cout << "Success [0.0 (worst),4.0(perfect)]: " << success << '\n'; }

 

 

 

 

 

Screen output

 

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Show how the neural net responds to input: (0,0): 0.531861 (and should get to 0.0)' (1,0): 0.518577 (and should get to 1.0)' (0,1): 0.543986 (and should get to 1.0)' (1,1): 0.530743 (and should get to 0.0)' Success [0.0 (worst),4.0(perfect)]: 1.99996