Package org.encog.ml.train

Examples of org.encog.ml.train.MLTrain.addStrategy()

107
108
109
110
111
112
113
114
115
116
117
    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()) {
View Full Code Here
108
109
110
111
112
113
114
115
116
117
118
    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();
View Full Code Here
109
110
111
112
113
114
115
116
117
118
119
    ((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
View Full Code Here
85
86
87
88
89
90
91
92
93
94
95
        XORSQL.SQL_PWD);
   
    // train the neural network
    final MLTrain train = new ResilientPropagation(network, trainingSet);
    // reset if improve is less than 1% over 5 cycles
    train.addStrategy(new RequiredImprovementStrategy(5));
   
    int epoch = 1;

    do {
      train.iteration();
View Full Code Here
250
251
252
253
254
255
256
257
258
259
260
    // third, create the trainer
    MLTrainFactory trainFactory = new MLTrainFactory(); 
    MLTrain train = trainFactory.create(method,dataSet,trainerName,trainerArgs);       
    // reset if improve is less than 1% over 5 cycles
    if( method instanceof MLResettable && !(train instanceof ManhattanPropagation) ) {
      train.addStrategy(new RequiredImprovementStrategy(500));
    }

    // fourth, train and evaluate.
    EncogUtility.trainToError(train, 0.01);
    EncogUtility.evaluate((MLRegression)method, dataSet);
View Full Code Here
107
108
109
110
111
112
113
114
115
116
117
    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()) {
View Full Code Here
108
109
110
111
112
113
114
115
116
117
118
    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();
View Full Code Here
109
110
111
112
113
114
115
116
117
118
119
    ((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
View Full Code Here
56
57
58
59
60
61
62
63
64
65
66
   
    // train the neural network
    final MLTrain train = new ResilientPropagation(network, trainingSet);
    // reset if improve is less than 1% over 5 cycles
    train.addStrategy(new RequiredImprovementStrategy(5));

    EncogUtility.trainToError(train, 0.01);
   
    EncogUtility.evaluate(network, trainingSet);
View Full Code Here
191
192
193
194
195
196
197
198
199
200
201
    MLTrain train = this.createTrainer(method, fold.getTraining());

    if (train.getImplementationType() == TrainingImplementationType.Iterative) {
      SimpleEarlyStoppingStrategy earlyStop = new SimpleEarlyStoppingStrategy(
          fold.getValidation());
      train.addStrategy(earlyStop);

      StringBuilder line = new StringBuilder();
      while (!train.isTrainingDone()) {
        train.iteration();
        line.setLength(0);
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. 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.