Examples of ResilientPropagation

org.encog.neural.networks.training.propagation.resilient.ResilientPropagation

One problem with the backpropagation algorithm is that the magnitude of the partial derivative is usually too large or too small. Further, the learning rate is a single value for the entire neural network. The resilient propagation learning algorithm uses a special update value(similar to the learning rate) for every neuron connection. Further these update values are automatically determined, unlike the learning rate of the backpropagation algorithm. For most training situations, we suggest that the resilient propagation algorithm (this class) be used for training. There are a total of three parameters that must be provided to the resilient training algorithm. Defaults are provided for each, and in nearly all cases, these defaults are acceptable. This makes the resilient propagation algorithm one of the easiest and most efficient training algorithms available. The optional parameters are: zeroTolerance - How close to zero can a number be to be considered zero. The default is 0.00000000000000001. initialUpdate - What are the initial update values for each matrix value. The default is 0.1. maxStep - What is the largest amount that the update values can step. The default is 50. Usually you will not need to use these, and you should use the constructor that does not require them. @author jheaton

    BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
    BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    
    // train network 1, no continue
    ResilientPropagation rprop1 = new ResilientPropagation(network1,trainingData);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    
    // train network 2, continue
    ResilientPropagation rprop2 = new ResilientPropagation(network2,trainingData);
    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new ResilientPropagation(network2,trainingData);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
    
    // verify weights are the same
    double[] weights1 = NetworkCODEC.networkToArray(network1);
    double[] weights2 = NetworkCODEC.networkToArray(network2);
    
    Assert.assertEquals(rprop1.getError(), rprop2.getError(), 0.01);
    Assert.assertEquals(weights1.length, weights2.length);
    Assert.assertArrayEquals(weights1, weights2, 0.01);
    
  }

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Examples of ResilientPropagation

Examples of org.encog.neural.networks.training.propagation.resilient.ResilientPropagation