Package org.encog.neural.networks.training.propagation.back

Examples of org.encog.neural.networks.training.propagation.back.Backpropagation.iteration()

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    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
   
    // verify weights are the same
    double[] weights1 = NetworkCODEC.networkToArray(network1);
    double[] weights2 = NetworkCODEC.networkToArray(network2);
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    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
   
    // verify weights are the same
    double[] weights1 = NetworkCODEC.networkToArray(network1);
    double[] weights2 = NetworkCODEC.networkToArray(network2);
   
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    BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    // train network 1, no continue
    Backpropagation rprop1 = new Backpropagation(network1,trainingData,0.4,0.4);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
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    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    // train network 1, no continue
    Backpropagation rprop1 = new Backpropagation(network1,trainingData,0.4,0.4);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
    Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
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    // train network 1, no continue
    Backpropagation rprop1 = new Backpropagation(network1,trainingData,0.4,0.4);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
    Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.iteration();
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    // train network 1, no continue
    Backpropagation rprop1 = new Backpropagation(network1,trainingData,0.4,0.4);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
    Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.iteration();
    rprop2.iteration();
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    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
    Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
View Full Code Here
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    rprop1.iteration();
   
    // train network 2, continue
    Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
View Full Code Here
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    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
   
    // verify weights are the same
    double[] weights1 = NetworkCODEC.networkToArray(network1);
    double[] weights2 = NetworkCODEC.networkToArray(network2);
View Full Code Here
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    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
   
    // verify weights are the same
    double[] weights1 = NetworkCODEC.networkToArray(network1);
    double[] weights2 = NetworkCODEC.networkToArray(network2);
   
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