Package org.encog.neural.networks

Examples of org.encog.neural.networks.BasicNetwork.reset()

113
114
115
116
117
118
119
120
121
122
123
    network.addLayer(hidden = new BasicLayer(this.activation, true,
        this.hiddenNeurons));
    network.addLayer(new BasicLayer(null, false, this.outputNeurons));
    input.setContextFedBy(hidden);
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }

  /**
   * Set the activation function to use on each of the layers.
View Full Code Here
116
117
118
119
120
121
122
123
124
125
126
        this.hiddenNeurons));
    network.addLayer(output = new BasicLayer(this.activation, false,
        this.outputNeurons));
    hidden.setContextFedBy(output);
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }

  /**
   * Set the activation function to use on each of the layers.
View Full Code Here
111
112
113
114
115
116
117
118
119
120
121
    final Layer output = new BasicLayer(this.activationOutput, false,
        this.outputNeurons);
    result.addLayer(output);

    result.getStructure().finalizeStructure();
    result.reset();

    return result;
  }

  /**
 
View Full Code Here
419
420
421
422
423
424
425
426
427
428
429
    // train the neural network

    double error = Double.POSITIVE_INFINITY;
    for (int z = 0; z < this.weightTries; z++) {
      network.reset();
      final Propagation train = new ResilientPropagation(network,
          useTraining);
      final StopTrainingStrategy strat = new StopTrainingStrategy(0.001,
          5);
View Full Code Here
36
37
38
39
40
41
42
43
44
45
46
      BasicNetwork network = new BasicNetwork();
      network.addLayer(new BasicLayer(null, false, 2));
      network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
      network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
      network.getStructure().finalizeStructure();
      network.reset();
      (new ConsistentRandomizer(0,0.5,i)).randomize(network);

      // create training data
      MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
View Full Code Here
128
129
130
131
132
133
134
135
136
137
138
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(WINDOW_SIZE));
    network.addLayer(new BasicLayer(10));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }
 
  public void train(BasicNetwork network,MLDataSet training)
  {
View Full Code Here
185
186
187
188
189
190
191
192
193
194
195
    if (hidden2 > 0) {
      pattern.addHiddenLayer(hidden2);
    }

    final BasicNetwork network = (BasicNetwork)pattern.generate();
    network.reset();
    return network;
  }

  /**
   * Train the neural network, using SCG training, and output status to the
View Full Code Here
48
49
50
51
52
53
54
55
56
57
58
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

    // train the neural network
    final MLTrain train = new ResilientPropagation(network, trainingSet);
View Full Code Here
71
72
73
74
75
76
77
78
79
80
81
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();

    MLDataSet trainingSet = new SQLNeuralDataSet(
        XORSQL.SQL,
        XORSQL.INPUT_SIZE,
        XORSQL.IDEAL_SIZE,
View Full Code Here
40
41
42
43
44
45
46
47
48
49
50
    final BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(MultiBench.INPUT_COUNT));
    network.addLayer(new BasicLayer(MultiBench.HIDDEN_COUNT));
    network.addLayer(new BasicLayer(MultiBench.OUTPUT_COUNT));
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }
 
  public static MLDataSet generateTraining()
  {
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. 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.