Examples of ManhattanPropagation

org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation
One problem that the backpropagation technique has is that the magnitude of the partial derivative may be calculated too large or too small. The Manhattan update algorithm attempts to solve this by using the partial derivative to only indicate the sign of the update to the weight matrix. The actual amount added or subtracted from the weight matrix is obtained from a simple constant. This constant must be adjusted based on the type of neural network being trained. In general, start with a higher constant and decrease it as needed. The Manhattan update algorithm can be thought of as a simplified version of the resilient algorithm. The resilient algorithm uses more complex techniques to determine the update value. @author jheaton

Examples of org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation

  public void testManhattan() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain bprop = new ManhattanPropagation(network, trainingData, 0.01);
    NetworkUtil.testTraining(bprop,0.01);
  }

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Examples of org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation


      if( kFold>0 ) {
        trainingData = this.wrapTrainingData(trainingData);
      }
      
      MLTrain train = new ManhattanPropagation(
          (BasicNetwork) file.getObject(), trainingData, learningRate);
      
      if( kFold>0 ) {
        train = this.wrapTrainer(trainingData,train,kFold);
      }

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Examples of org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation

    final ParamsHolder holder = new ParamsHolder(args);


    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.1);


    return new ManhattanPropagation((BasicNetwork) method, training,
        learningRate);
  }

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Examples of org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation

  public void testManhattan() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    MLTrain bprop = new ManhattanPropagation(network, trainingData, 0.01);
    NetworkUtil.testTraining(trainingData,bprop,0.01);
  }

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Examples of org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation

    return this.learningRate;
  }


  @Override
  public MLTrain getTraining(MLMethod mlMethod, MLDataSet trainingData) {
    return (MLTrain) new ManhattanPropagation((BasicNetwork) mlMethod, trainingData, 0.01);
  }

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Examples of org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation

    final ParamsHolder holder = new ParamsHolder(args);


    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.1);


    return new ManhattanPropagation((BasicNetwork) method, training,
        learningRate);
  }

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