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

Examples of org.encog.neural.networks.training.propagation.resilient.ResilientPropagation.iteration()

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    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont);
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    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont);
   
    rprop1.iteration();
    rprop3.iteration();
   
   
    for(int i=0;i<net1.getFlat().getWeights().length;i++) {
      Assert.assertEquals(net1.getFlat().getWeights()[i], net2.getFlat().getWeights()[i],0.0001);
    }
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    ResilientPropagation rprop2 = new ResilientPropagation(net2,trainingSet);
   
    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, cont);
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    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, cont);
    TrainingContinuation cont2 = (TrainingContinuation)EncogDirectoryPersistence.loadObject(EG_FILENAME);
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    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont2);
   
    rprop1.iteration();
    rprop3.iteration();
   
   
    for(int i=0;i<net1.getFlat().getWeights().length;i++) {
      Assert.assertEquals(net1.getFlat().getWeights()[i], net2.getFlat().getWeights()[i],0.0001);
    }
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  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();   
   
    ResilientPropagation rprop = new ResilientPropagation(network,trainingData);
    rprop.iteration();
    rprop.iteration();
    network.enableConnection(1, 0, 0, false);
    network.enableConnection(1, 1, 0, false);
   
    Assert.assertTrue(network.getStructure().isConnectionLimited());
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    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();   
   
    ResilientPropagation rprop = new ResilientPropagation(network,trainingData);
    rprop.iteration();
    rprop.iteration();
    network.enableConnection(1, 0, 0, false);
    network.enableConnection(1, 1, 0, false);
   
    Assert.assertTrue(network.getStructure().isConnectionLimited());
   
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    Assert.assertTrue(network.getStructure().isConnectionLimited());
   
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    // these connections were removed, and should not have been "trained"
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
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    Assert.assertTrue(network.getStructure().isConnectionLimited());
   
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    // these connections were removed, and should not have been "trained"
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);   
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    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    // these connections were removed, and should not have been "trained"
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);   
    rprop.finishTraining();
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