示例#1
0
  private void computeTotalGradient(
      Gradients totalGradients, Gradients partialGradients, SetOfIOPairs trainingSet) {
    // na zaciatku sa inicializuju gradienty (total)
    totalGradients.resetGradients();
    // partialGradients.resetGradients();
    // Gradients totalGradients = new Gradients(this);
    // Gradients partialGradients = new Gradients(this); /***/

    for (SetOfIOPairs.IOPair pair : trainingSet.pairs) { // pre kazdy par trenovacej mnoziny
      // partialGradients = computeGradient(pair.inputs, pair.outputs);
      computeGradient(partialGradients, pair.inputs, pair.outputs);
      for (int il = this.numberOfLayers() - 1; il >= 1; il--) { // pre vsetky vrstvy okrem poslednej
        NeuralLayer currentLayer = this.getLayer(il);
        for (int in = 0;
            in < currentLayer.numberOfNeurons();
            in++) { // pre vsetky neurony na currentLayer
          // upravime gradient prahov :
          totalGradients.incrementThreshold(il, in, partialGradients.getThreshold(il, in));
          for (int ii = 0;
              ii < currentLayer.lowerLayer().numberOfNeurons();
              ii++) { // pre vsetky vstupy
            totalGradients.incrementWeight(il, in, ii, partialGradients.getWeight(il, in, ii));
          }
        }
      } // end for layer
    } // end foreach
    // return totalGradients;
  } // end method
示例#2
0
  private void adaptation(
      SetOfIOPairs trainingSet, int maxK, double eps, double lambda, double micro) {
    //         trainingSet  : trenovacia mnozina
    //         maxK         : maximalny pocet iteracii
    //         eps          : pozadovana presnost normovanej dlzky gradientu
    //         lambda       : rychlost ucenia (0.1)
    //         micro        : momentovy clen
    double delta;
    Gradients deltaGradients = new Gradients(this);
    Gradients totalGradients = new Gradients(this);
    Gradients partialGradients = new Gradients(this);

    System.out.println("setting up random weights and thresholds ...");

    // prahy a vahy neuronovej siete nastavime na nahodne hodnoty, delta-gradienty vynulujeme (oni
    // sa nuluju uz pri init)
    for (int il = this.numberOfLayers() - 1;
        il >= 1;
        il--) { // iteracia cez vsetky vrstvy nadol okrem poslednej
      NeuralLayer currentLayer = this.getLayer(il);
      for (int in = 0; in < currentLayer.numberOfNeurons(); in++) { // pre kazdy neuron na vrstve
        Neuron currentNeuron = currentLayer.getNeuron(in);
        currentNeuron.threshold = 2 * this.random() - 1;
        // deltaGradients.setThreshold(il,in,0.0);
        for (int ii = 0; ii < currentNeuron.numberOfInputs(); ii++) {
          currentNeuron.getInput(ii).weight = 2 * this.random() - 1;
          // deltaGradients.setWeight(il,in,ii,0.0);
        } // end ii
      } // end in
    } // end il

    int currK = 0; // citac iteracii
    double currE =
        Double.POSITIVE_INFINITY; // pociatocna aktualna presnost bude nekonecna (tendencia
    // znizovania)

    System.out.println("entering adaptation loop ... (maxK = " + maxK + ")");

    while (currK < maxK && currE > eps) {
      computeTotalGradient(totalGradients, partialGradients, trainingSet);
      for (int il = this.numberOfLayers() - 1;
          il >= 1;
          il--) { // iteracia cez vsetky vrstvy nadol okrem poslednej
        NeuralLayer currentLayer = this.getLayer(il);

        for (int in = 0; in < currentLayer.numberOfNeurons(); in++) { // pre kazdy neuron na vrstve
          Neuron currentNeuron = currentLayer.getNeuron(in);
          delta =
              -lambda * totalGradients.getThreshold(il, in)
                  + micro * deltaGradients.getThreshold(il, in);
          currentNeuron.threshold += delta;
          deltaGradients.setThreshold(il, in, delta);
        } // end for ii 1

        for (int in = 0; in < currentLayer.numberOfNeurons(); in++) { // pre kazdy neuron na vrstve
          Neuron currentNeuron = currentLayer.getNeuron(in);
          for (int ii = 0; ii < currentNeuron.numberOfInputs(); ii++) { // a pre kazdy vstup neuronu
            delta =
                -lambda * totalGradients.getWeight(il, in, ii)
                    + micro * deltaGradients.getWeight(il, in, ii);
            currentNeuron.getInput(ii).weight += delta;
            deltaGradients.setWeight(il, in, ii, delta);
          } // end for ii
        } // end for in 2
      } // end for il

      currE = totalGradients.getGradientAbs();
      currK++;
      if (currK % 25 == 0) System.out.println("currK=" + currK + "   currE=" + currE);
    } // end while
  }
示例#3
0
  private void computeGradient(
      Gradients gradients, Vector<Double> inputs, Vector<Double> requiredOutputs) {
    // Gradients gradients = new Gradients(this);
    activities(inputs);
    for (int il = this.numberOfLayers() - 1;
        il >= 1;
        il--) { // backpropagation cez vsetky vrstvy okrem poslednej
      NeuralLayer currentLayer = this.getLayer(il);

      if (currentLayer.isLayerTop()) { // ak sa jedna o najvyssiu vrstvu
        // pridame gradient prahov pre danu vrstvu do odpovedajuceho vektora a tento gradient
        // pocitame cez neurony :
        // gradients.thresholds.add(il, new Vector<Double>());
        for (int in = 0;
            in < currentLayer.numberOfNeurons();
            in++) { // pre vsetky neurony na vrstve
          Neuron currentNeuron = currentLayer.getNeuron(in);
          gradients.setThreshold(
              il,
              in,
              currentNeuron.output
                  * (1 - currentNeuron.output)
                  * (currentNeuron.output - requiredOutputs.elementAt(in)));
        } // end for each neuron

        for (int in = 0; in < currentLayer.numberOfNeurons(); in++) { // for each neuron
          Neuron currentNeuron = currentLayer.getNeuron(in);
          for (int ii = 0; ii < currentNeuron.numberOfInputs(); ii++) { // for each neuron's input
            NeuralInput currentInput = currentNeuron.getInput(ii);
            gradients.setWeight(
                il,
                in,
                ii,
                gradients.getThreshold(il, in) * currentLayer.lowerLayer().getNeuron(ii).output);
          } // end for each input
        } // end for each neuron

      } else { // ak sa jedna o spodnejsie vrstvy (najnizsiu vrstvu nepocitame, ideme len po 1.)
        // pocitame gradient prahov :
        // gradients.thresholds.add(il, new Vector<Double>());
        for (int in = 0; in < currentLayer.numberOfNeurons(); in++) { // for each neuron
          double aux = 0;
          // iterujeme cez vsetky axony neuronu (resp. synapsie neuronov na vyssej vrstve)
          for (int ia = 0; ia < currentLayer.upperLayer().numberOfNeurons(); ia++) {
            aux +=
                gradients.getThreshold(il + 1, ia)
                    * currentLayer.upperLayer().getNeuron(ia).getInput(in).weight;
          }
          gradients.setThreshold(
              il,
              in,
              currentLayer.getNeuron(in).output * (1 - currentLayer.getNeuron(in).output) * aux);
        } // end for each neuron

        // pocitame gradienty vah :
        for (int in = 0; in < currentLayer.numberOfNeurons(); in++) { // for each neuron
          Neuron currentNeuron = currentLayer.getNeuron(in);
          for (int ii = 0; ii < currentNeuron.numberOfInputs(); ii++) { // for each neuron's input
            NeuralInput currentInput = currentNeuron.getInput(ii);
            gradients.setWeight(
                il,
                in,
                ii,
                gradients.getThreshold(il, in) * currentLayer.lowerLayer().getNeuron(ii).output);
          } // end for each input
        } // end for each neuron
      } // end layer IF
    } // end backgropagation for each layer
    // return gradients;
  }