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

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

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    for(int i=1;i<=20;i++)
    {
      train.iteration();
      System.out.println("Iteration #" + i + " Error:" + train.getError());
    }
    train.finishTraining();
    long stop = System.currentTimeMillis();
    double diff = ((double)(stop - start))/1000.0;
    System.out.println("RPROP Result:" + diff + " seconds." );
    System.out.println("Final RPROP error: " + network.calculateError(data));
    return diff;
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    for(int i=1;i<=20;i++)
    {
      train.iteration();
      System.out.println("Iteration #" + i + " Error:" + train.getError());
    }
    train.finishTraining();
    long stop = System.currentTimeMillis();
    double diff = ((double)(stop - start))/1000.0;
    System.out.println("MPROP Result:" + diff + " seconds." );
    System.out.println("Final MPROP error: " + network.calculateError(data));
    return diff;
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    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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    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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