Package com.github.neuralnetworks.samples.xor

Examples of com.github.neuralnetworks.samples.xor.XorOutputError

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  // create training and testing input providers
  XorInputProvider trainingInput = new XorInputProvider(10000);
  XorInputProvider testingInput = new XorInputProvider(4);

  // create backpropagation trainer for the network
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainingInput, testingInput, new XorOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 1f, 0.5f, 0f, 0f);

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

  // early stopping
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  // create training and testing input providers
  SimpleInputProvider input = new SimpleInputProvider(new float[][] { {0, 0}, {0, 1}, {1, 0}, {1, 1} }, new float[][] { {0}, {1}, {1}, {0} });

  // create backpropagation trainer for the network
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, input, input, new XorOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.1f, 0.9f, 0f, 0f, 0f, 1, 1, 100000);

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

  // early stopping
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  // create training and testing input providers
  SimpleInputProvider input = new SimpleInputProvider(new float[][] { {0, 0}, {0, 1}, {1, 0}, {1, 1} }, new float[][] { {0}, {1}, {1}, {0} });

  // 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
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  mlpfco.getWeights().set(0.08f, 0, 1);

  // compare bp
  SimpleInputProvider inputProvider = new SimpleInputProvider(new float[][] { {0, 0}, {0, 1}, {1, 0}, {1, 1} }, new float[][] { {0}, {1}, {1}, {0} });

  BackPropagationTrainer<?> mlpbpt = TrainerFactory.backPropagation(mlp, inputProvider, inputProvider, new XorOutputError(), null, 1f, 0f, 0f, 0f, 0f, 1, 1, 10000);
  mlpbpt.train();
  mlpbpt.test();

  BackPropagationTrainer<?> cnnbpt = TrainerFactory.backPropagation(cnn, inputProvider, inputProvider, new XorOutputError(), null, 1f, 0f, 0f, 0f, 0f, 1, 1, 10000);
  cnnbpt.train();
  cnnbpt.test();

  assertEquals(mlpbpt.getOutputError().getTotalNetworkError(), cnnbpt.getOutputError().getTotalNetworkError(), 0);
  assertTrue(Arrays.equals(cnnfco.getWeights().getElements(), mlpfco.getWeights().getElements()));
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Related Classes of com.github.neuralnetworks.samples.xor.XorOutputError

  • com.github.neuralnetworks.samples.test.XorTest
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