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

Examples of org.encog.neural.networks.training.propagation.back.Backpropagation

103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
    // train the neural network
    CalculateScore score = new TrainingSetScore(trainingSet);
    final MLTrain trainAlt = new NeuralSimulatedAnnealing(
        network, score, 10, 2, 100);

    final MLTrain trainMain = new Backpropagation(network, trainingSet,0.000001, 0.0);

    ((Propagation)trainMain).setNumThreads(1);
    final StopTrainingStrategy stop = new StopTrainingStrategy();
    trainMain.addStrategy(new Greedy());
    trainMain.addStrategy(new HybridStrategy(trainAlt));
    trainMain.addStrategy(stop);

    int epoch = 0;
    while (!stop.shouldStop()) {
      trainMain.iteration();
      System.out.println("Training " + what + ", Epoch #" + epoch
          + " Error:" + trainMain.getError());
      epoch++;
    }
    return trainMain.getError();
  }
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
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
    network.reset();

    MLDataSet trainingSet = new BasicMLDataSet(input, output);

    // train the neural network
    MLTrain train = new Backpropagation(network, trainingSet, 0.7, 0.7);

    Stopwatch sw = new Stopwatch();
    sw.start();
    // run epoch of learning procedure
    for (int i = 0; i < ITERATIONS; i++) {
      train.iteration();
    }
    sw.stop();

    return sw.getElapsedMilliseconds();
  }
View Full Code Here
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
    // train the neural network
    CalculateScore score = new TrainingSetScore(trainingSet);
    final MLTrain trainAlt = new NeuralSimulatedAnnealing(
        network, score, 10, 2, 100);

    final MLTrain trainMain = new Backpropagation(network, trainingSet,0.000001, 0.0);

    ((Propagation)trainMain).setNumThreads(1);
    final StopTrainingStrategy stop = new StopTrainingStrategy();
    trainMain.addStrategy(new Greedy());
    trainMain.addStrategy(new HybridStrategy(trainAlt));
    trainMain.addStrategy(stop);

    int epoch = 0;
    while (!stop.shouldStop()) {
      trainMain.iteration();
      System.out.println("Training " + what + ", Epoch #" + epoch
          + " Error:" + trainMain.getError());
      epoch++;
    }
    return trainMain.getError();
  }
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
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
    BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
    BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    // train network 1, no continue
    Backpropagation rprop1 = new Backpropagation(network1,trainingData,0.4,0.4);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
    Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
   
    // verify weights are the same
    double[] weights1 = NetworkCODEC.networkToArray(network1);
    double[] weights2 = NetworkCODEC.networkToArray(network2);
   
    Assert.assertEquals(rprop1.getError(), rprop2.getError(), 0.01);
    Assert.assertEquals(weights1.length, weights2.length);
    Assert.assertArrayEquals(weights1, weights2, 0.01);
   
  }
View Full Code Here
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
          

          
           BasicNetwork network3 = NetworkUtil.createXORNetworknNguyenWidrowUntrained();
          
           MLTrain bpropNguyen = new Backpropagation( network3, trainingData3, 0.9, 0.8 );    
           train(i, bpropNguyen, "NguyenWidrowRandomizer" );
          
           BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
          
           MLTrain bpropRange = new Backpropagation( network2, trainingData2, 0.9, 0.8 );    
           train(i, bpropRange,  "RangeRandomizer       ");
       }
   }
View Full Code Here
73
74
75
76
77
78
79
80
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();

    MLTrain bprop = new Backpropagation(network, trainingData, 0.7, 0.9);
    NetworkUtil.testTraining(bprop,0.01);
  }
View Full Code Here
297
298
299
300
301
302
303
304
305
306
307
     
      if( kFold>0 ) {
        trainingData = this.wrapTrainingData(trainingData);
      }

      MLTrain train = new Backpropagation((BasicNetwork) file.getObject(),
          trainingData, learningRate, momentum);
     
      if( kFold>0 ) {
        train = this.wrapTrainer(trainingData,train,kFold);
      }
View Full Code Here
60
61
62
63
64
65
66
67
    final double learningRate = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.7);
    final double momentum = holder.getDouble(
        MLTrainFactory.PROPERTY_LEARNING_MOMENTUM, false, 0.3);

    return new Backpropagation((BasicNetwork) method, training,
        learningRate, momentum);
  }
View Full Code Here
TOP

Related Classes of org.encog.neural.networks.training.propagation.back.Backpropagation

  • org.encog.ensemble.training.BackpropagationFactory
  • org.encog.examples.neural.benchmark.FahlmanEncoder
  • org.encog.examples.neural.benchmark.SimpleBenchmark
  • org.encog.examples.neural.recurrent.elman.ElmanXOR
  • org.encog.examples.neural.recurrent.jordan.JordanXOR
  • org.encog.ml.factory.train.BackPropFactory
  • org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation
  • org.encog.neural.networks.training.concurrent.jobs.BPROPJob
  • org.encog.neural.networks.training.EvaluateNuguyenWidrow
  • org.encog.neural.networks.training.propagation.TrainingContinuation
    • 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.