Package org.neuroph.core.transfer

Examples of org.neuroph.core.transfer.TransferFunction

55
56
57
58
59
60
61
62
63
64
65
                    inputFunction = new InputFunction(weightsFunction, summingFunction);                   
                }



    TransferFunction transferFunction = createTransferFunction(neuronProperties.getTransferFunctionProperties());

    Neuron neuron = null;
    Class neuronClass = neuronProperties.getNeuronType();

                // use two param constructor to create neuron
View Full Code Here
135
136
137
138
139
140
141
142
143
144
145
   * @param tfProperties
   *            transfer function properties
   * @return returns transfer function
   */
  private static TransferFunction createTransferFunction(Properties tfProperties) {
    TransferFunction transferFunction = null;

    Class  tfClass = (Class)tfProperties.getProperty("transferFunction");

                    try {
                        Class[] paramTypes = null;
View Full Code Here
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
        @Override
  protected void adjustOutputNeurons(double[] patternError) {
           
                // get output layer
                MatrixMlpLayer outputLayer = (MatrixMlpLayer)matrixLayers[matrixLayers.length - 1];
                TransferFunction transferFunction = outputLayer.getTransferFunction();

                // get output vector
                double[] outputs = outputLayer.getOutputs();
                double[] netInputs = outputLayer.getNetInput();
                double[] neuronErrors = outputLayer.getErrors(); // these will hold  -should be set from here!!!!

                // calculate errors(deltas) for all output neurons
                for(int i = 0; i < outputs.length; i++) {
                    neuronErrors[i] = patternError[i] * transferFunction.getDerivative(netInputs[i]); // ovde mi treba weighted sum, da ne bi morao ponovo da racunam
                }

                // update weights
                this.updateLayerWeights(outputLayer, neuronErrors);
        }
View Full Code Here
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
            int layersCount = matrixMlp.getLayersCount();
           
            for ( int layerIdx = layersCount -2 ; layerIdx > 0 ; layerIdx--) {
               MatrixMlpLayer currentLayer = (MatrixMlpLayer)matrixLayers[layerIdx];

               TransferFunction transferFunction = currentLayer.getTransferFunction();
               int neuronsCount = currentLayer.getNeuronsCount();

               double[] neuronErrors = currentLayer.getErrors();
               double[] netInputs = currentLayer.getNetInput();

               MatrixMlpLayer nextLayer = (MatrixMlpLayer)currentLayer.getNextLayer();
               double[] nextLayerErrors = nextLayer.getErrors();
               double[][] nextLayerWeights = nextLayer.getWeights();

               // calculate error for each neuron in current layer
               for(int neuronIdx = 0; neuronIdx < neuronsCount; neuronIdx++) {
                   // calculate weighted sum of errors of all neuron it is attached to - calculate how much this neuron is contributing to errors in next layer
                   double weightedErrorsSum = 0;

                   for(int nextLayerNeuronIdx = 0; nextLayerNeuronIdx < nextLayer.getNeuronsCount(); nextLayerNeuronIdx++) {
                     weightedErrorsSum += nextLayerErrors[nextLayerNeuronIdx] * nextLayerWeights[nextLayerNeuronIdx][neuronIdx];
                   }

                   // calculate the error for this neuron
                   neuronErrors[neuronIdx] = transferFunction.getDerivative(netInputs[neuronIdx]) * weightedErrorsSum;
               } // neuron iterator

               this.updateLayerWeights(currentLayer, neuronErrors);
              
            } // layer iterator
View Full Code Here
91
92
93
94
95
96
97
98
99
100
101
      double d = connection.getToNeuron().getError()
          * connection.getWeight().getValue();
      delta_sum += d; // weighted sum from the next layer
    } // for

    TransferFunction transferFunction = neuron.getTransferFunction();
    double netInput = neuron.getNetInput();
    double f1 = transferFunction.getDerivative(netInput);
    double delta = f1 * delta_sum;
    return delta;
  }
View Full Code Here
70
71
72
73
74
75
76
77
78
79
80
81
82
        neuron.setError(0);
                                i++;
        continue;
      }
     
      TransferFunction transferFunction = neuron.getTransferFunction();
      double neuronInput = neuron.getNetInput();
      double delta = outputError * transferFunction.getDerivative(neuronInput);
      neuron.setError(delta);
      this.updateNeuronWeights(neuron);       
      i++;
    } // for       
  }
View Full Code Here
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
  private static FlatLayer flattenLayer(Layer layer) {
    boolean inputLayer = false;
    Set<Class<?>> transferFunctions = new HashSet<Class<?>>();
    int neuronCount = 0;
    int biasCount = 0;
    TransferFunction transfer = null;

    for (Neuron neuron : layer.getNeurons()) {
      if (neuron.getClass() == InputNeuron.class)
        inputLayer = true;

      if (neuron.getClass() == Neuron.class
          || neuron.getClass() == InputNeuron.class) {
        neuronCount++;

        transfer = neuron.getTransferFunction();
        transferFunctions.add(transfer.getClass());
      } else if (neuron.getClass() == BiasNeuron.class)
        biasCount++;
    }

    if (transferFunctions.size() > 1)
View Full Code Here
TOP

Related Classes of org.neuroph.core.transfer.TransferFunction

  • org.neuroph.contrib.matrixmlp.MatrixMomentumBackpropagation
  • org.neuroph.nnet.flat.FlatNetworkPlugin
  • org.neuroph.nnet.learning.BackPropagation
  • org.neuroph.nnet.learning.SigmoidDeltaRule
  • org.neuroph.util.NeuronFactory
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.