Package org.encog.neural.networks

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

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    network.addLayer(new BasicLayer(new ActivationSigmoid(), true,
        HIDDEN_COUNT));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false,
        output[0].length));
    network.getStructure().finalizeStructure();
    network.reset();

    MLDataSet trainingSet = new BasicMLDataSet(input, output);

    // train the neural network
    MLTrain train = new Backpropagation(network, trainingSet, 0.7, 0.7);
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    network.addLayer(new BasicLayer(Constants.INPUT_NEURON_COUNT));
    network.addLayer(new BasicLayer(60));
    //network.addLayer(new BasicLayer(30));
    network.addLayer(new BasicLayer(Constants.OUTPUT_NEURON_COUNT));
    network.getStructure().finalizeStructure();
    network.reset();
   
    NeuralMouse mouse = new NeuralMouse(network,maze);
    return mouse;
  }
 
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    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) {
    final FoldedDataSet folded = new FoldedDataSet(training);
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    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
    network.reset();

    // create training data
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
   
    // train the neural network
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    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();
   
    final MLDataSet training = RandomTrainingFactory.generate(1000,50000,
        INPUT_COUNT, OUTPUT_COUNT, -1, 1);
   
    ResilientPropagation rprop = new ResilientPropagation(network,training);
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    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);
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public class TestClone extends TestCase {

  public void testClone() throws Throwable
  {
    BasicNetwork source = XOR.createThreeLayerNet();
    source.reset();
   
    BasicNetwork target = (BasicNetwork)source.clone();
   
    TestCase.assertTrue(target.equals(source));
  }
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    pattern.setInputNeurons(3);
    pattern.addHiddenLayer(50);
    pattern.setOutputNeurons(1);
    pattern.setActivationFunction(new ActivationTANH());
    BasicNetwork network = (BasicNetwork)pattern.generate();
    network.reset();
    return network;
  }
 
  public static void main(String args[])
  {
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    final BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null, true, 2));
    network.addLayer(new BasicLayer(new ActivationSigmoidPosNeg(), true, 4));
    network.addLayer(new BasicLayer(new ActivationSigmoidPosNeg(), true, 1));
    network.getStructure().finalizeStructure();
    network.reset();

    final MLDataSet trainingSet = new BasicMLDataSet(
        CustomActivation.XOR_INPUT, CustomActivation.XOR_IDEAL);

   
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      }
    }

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

    return result;
  }

}
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