Examples of TrainFlatNetworkBackPropagation

Examples of org.encog.engine.network.train.prop.TrainFlatNetworkBackPropagation

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          this.training = new TrainFlatNetworkManhattan(this.flat,
              this.lastTrainingSet, this.learningRate);
          break;
         
        case BackPropagation:
          this.training = new TrainFlatNetworkBackPropagation(this.flat,
              this.lastTrainingSet, this.getLearningRate(), this.getMomentum());         
          break;
      }
     
      this.training.setNumThreads(this.numThreads);
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation

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    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);

    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);

    double[] a = new double[2];
    double[] b = new double[1];

    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();
  }
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation

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    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);

    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);

    double[] a = new double[2];
    double[] b = new double[1];

    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();
  }
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation

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  public Backpropagation(final ContainsFlat network,
      final MLDataSet training, final double learnRate,
      final double momentum) {
    super(network, training);
    ValidateNetwork.validateMethodToData(network, training);
    final TrainFlatNetworkBackPropagation backFlat = new TrainFlatNetworkBackPropagation(
        network.getFlat(), getTraining(), learnRate, momentum);
    setFlatTraining(backFlat);

  }
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation

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   */
  @Override
  public final TrainingContinuation pause() {
    final TrainingContinuation result = new TrainingContinuation();
    result.setTrainingType(this.getClass().getSimpleName());
    final TrainFlatNetworkBackPropagation backFlat = (TrainFlatNetworkBackPropagation) getFlatTraining();
    final double[] d = backFlat.getLastDelta();
    result.set(Backpropagation.LAST_DELTA, d);
    return result;
  }
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Examples of org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation

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    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);

    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);

    double[] a = new double[2];
    double[] b = new double[1];

    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();
  }
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