Package com.github.neuralnetworks.training.events

Examples of com.github.neuralnetworks.training.events.LogTrainingListener

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  // create backpropagation trainer for the network
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, input, input, new XorOutputError(), null, 1f, 0.5f, 0f, 0f, 0f, 1, 1, 5000);

  // add logging
  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName()));

  // test
  bpt.test();

  assertEquals(0, bpt.getOutputError().getTotalNetworkError(), 0.1);
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  // Contrastive divergence training
  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 1, 1, 100, false);

  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), true, false));

  // training
  t.train();

  // testing
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  // Persistent Contrastive divergence trainer
  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 1, 1, 100, true);

  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), true, false));

  // training
  t.train();

  // testing
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Related Classes of com.github.neuralnetworks.training.events.LogTrainingListener

  • com.github.neuralnetworks.calculation.OutputError
  • com.github.neuralnetworks.samples.test.CifarTest
  • com.github.neuralnetworks.samples.test.IrisTest
  • com.github.neuralnetworks.samples.test.MnistTest
  • com.github.neuralnetworks.samples.test.XorTest
  • com.github.neuralnetworks.tensor.Matrix
  • com.github.neuralnetworks.test.AETest
  • com.github.neuralnetworks.test.RBMTest
  • com.github.neuralnetworks.util.Matrix
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