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

Examples of org.encog.neural.networks.training.propagation.Propagation

420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
    // train the neural network

    double error = Double.POSITIVE_INFINITY;
    for (int z = 0; z < this.weightTries; z++) {
      network.reset();
      final Propagation train = new ResilientPropagation(network,
          useTraining);
      final StopTrainingStrategy strat = new StopTrainingStrategy(0.001,
          5);

      train.addStrategy(strat);
      train.setNumThreads(1); // force single thread mode

      for (int i = 0; (i < this.iterations) && !getShouldStop()
          && !strat.shouldStop(); i++) {
        train.iteration();
      }

      error = Math.min(error, train.getError());
    }

    if (buffer != null) {
      buffer.close();
    }
View Full Code Here
182
183
184
185
186
187
188
189
190
   * @param trainingSet
   *            The training set to use.
   */
  public static void trainDialog(final BasicNetwork network,
      final MLDataSet trainingSet) {
    final Propagation train = new ResilientPropagation(network, trainingSet);
    train.setNumThreads(0);
    TrainingDialog.trainDialog(train, network, trainingSet);
  }
View Full Code Here
202
203
204
205
206
207
208
209
210
   * @param minutes
   *            The number of minutes to train for.
   */
  public static void trainConsole(final BasicNetwork network,
      final MLDataSet trainingSet, final int minutes) {
    final Propagation train = new ResilientPropagation(network, trainingSet);
    train.setNumThreads(0);
    EncogUtility.trainConsole(train, network, trainingSet, minutes);
  }
View Full Code Here
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
    for(int i=0;i<TRIES;i++) {
   
      MLMethod method = EncogUtility.simpleFeedForward(INPUT_OUTPUT_COUNT,
          HIDDEN_COUNT, 0, INPUT_OUTPUT_COUNT, false);
     
      Propagation train = new Backpropagation((ContainsFlat)method, trainingData,1.7,0);
      //Propagation train = new ResilientPropagation((ContainsFlat)method, trainingData);
      ((Propagation)train).fixFlatSpot(true);
     
      int iteration = 0;
      do {
        train.iteration();
       
        iteration++;
      } while( train.getError()>0.01 );
      count[i] = iteration;
      System.out.println("Begin Try #" + (i+1) + ", took " + iteration + " iterations.");     
    }
   
    System.out.println("Tries: " + TRIES);
View Full Code Here
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
  /**
   * {@inheritDoc}
   */
  @Override
  public final void createTrainer(final boolean singleThreaded) {
    final Propagation train = new ResilientPropagation(getNetwork(),
        getTraining(), getInitialUpdate(), getMaxStep());

    if (singleThreaded) {
      train.setNumThreads(1);
    } else {
      train.setNumThreads(0);
    }

    for (final Strategy strategy : getStrategies()) {
      train.addStrategy(strategy);
    }

    setTrain(train);
  }
View Full Code Here
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
  /**
   * {@inheritDoc}
   */
  @Override
  public final void createTrainer(final boolean singleThreaded) {
    final Propagation train = new Backpropagation(getNetwork(),
        getTraining(), getLearningRate(), getMomentum());

    if (singleThreaded) {
      train.setNumThreads(1);
    } else {
      train.setNumThreads(0);
    }

    for (final Strategy strategy : getStrategies()) {
      train.addStrategy(strategy);
    }

    setTrain(train);
  }
View Full Code Here
205
206
207
208
209
210
211
212
213
   * @param minutes
   *            The number of minutes to train for.
   */
  public static void trainConsole(final BasicNetwork network,
      final MLDataSet trainingSet, final int minutes) {
    final Propagation train = new ResilientPropagation(network, trainingSet);
    train.setThreadCount(0);
    EncogUtility.trainConsole(train, network, trainingSet, minutes);
  }
View Full Code Here
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
    // train the neural network

    double error = Double.POSITIVE_INFINITY;
    for (int z = 0; z < this.weightTries; z++) {
      network.reset();
      final Propagation train = new ResilientPropagation(network,
          useTraining);
      final StopTrainingStrategy strat = new StopTrainingStrategy(0.001,
          5);

      train.addStrategy(strat);
      train.setThreadCount(1); // force single thread mode

      for (int i = 0; (i < this.iterations) && !getShouldStop()
          && !strat.shouldStop(); i++) {
        train.iteration();
      }

      error = Math.min(error, train.getError());
    }

    if (buffer != null) {
      buffer.close();
    }
View Full Code Here
181
182
183
184
185
186
187
188
189
   * @param trainingSet
   *            The training set to use.
   */
  public static void trainDialog(final BasicNetwork network,
      final MLDataSet trainingSet) {
    final Propagation train = new ResilientPropagation(network, trainingSet);
    train.setThreadCount(0);
    TrainingDialog.trainDialog(train, network, trainingSet);
  }
View Full Code Here
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
  /**
   * {@inheritDoc}
   */
  @Override
  public void createTrainer(final boolean singleThreaded) {
    final Propagation train = new ResilientPropagation(getNetwork(),
        getTraining(), getInitialUpdate(), getMaxStep());

    if (singleThreaded) {
      train.setThreadCount(1);
    } else {
      train.setThreadCount(0);
    }

    for (final Strategy strategy : getStrategies()) {
      train.addStrategy(strategy);
    }

    setTrain(train);
  }
View Full Code Here
TOP

Related Classes of org.encog.neural.networks.training.propagation.Propagation

  • org.encog.EncogError
  • org.encog.engine.network.activation.ActivationFunction
  • org.encog.examples.neural.benchmark.FahlmanEncoder
  • org.encog.neural.networks.training.concurrent.jobs.BPROPJob
  • org.encog.neural.networks.training.concurrent.jobs.RPROPJob
  • org.encog.neural.prune.PruneIncremental
  • org.encog.platformspecific.j2se.TrainingDialog
  • org.encog.util.concurrency.DetermineWorkload
  • org.encog.util.concurrency.TaskGroup
  • org.encog.util.simple.EncogUtility
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.