root/trunk/test_xor.d

/** A simple XOR test.  */


import and.api;
import and.platform;


const real [] [] inputs  = [
                [ 0,0 ],
                [ 0,1 ],
                [ 1,0 ],
                [ 1,1 ]             

];


const real [] [] outputs = [ 
                [ 0 ],
                [ 1 ],
                [ 1 ],
                [ 0 ]
];


void main ()
{

  try 
    {


      IActivationFunction f = new TanhActivationFunction;


      Layer input = new Layer(2,0);
      Layer [] hidden = [ new Layer(4,2,f)  ];
      Layer output = new Layer(1,4,f) ;

      NeuralNetwork nn = new NeuralNetwork(input,hidden,output );
      CostFunction cost = new SSE();
      BackPropagation bp = new BackPropagation(nn,cost);

      bp.epochs = uint.max;
      bp.learningRate = 0.1;
      bp.momentum = 0.5;
      bp.errorThreshold = 0.0001;

      void callback( uint currentEpoch, real currentError  )
    {
      Stdout.formatln("Epoch: [ {0} ] | Error [ {1} ] ",currentEpoch, currentError );
    }

      bp.setProgressCallback(&callback, 100 );
      
      bp.train(inputs,outputs);

      Stdout.formatln("{0}",bp.actualEpochs);
      Stdout.formatln("{0}" ,bp.computeOutput(inputs[0])[0]);
      Stdout.formatln("{0}" ,bp.computeOutput(inputs[1])[0]);
      Stdout.formatln("{0}" ,bp.computeOutput(inputs[2])[0]);
      Stdout.formatln("{0}" ,bp.computeOutput(inputs[3])[0]);
    }
  catch ( Exception e )
    {
      Stdout.formatln("Exception: {0}",e.toUtf8() );
    }

}