Examples with LinearErrorFunction org.encog.neural.error.LinearErrorFunction used on opensource projects

Example 1 with LinearErrorFunction

use of org.encog.neural.error.LinearErrorFunction in project shifu by ShifuML.

the class AbstractNNWorker method initGradient.

@SuppressWarnings("unchecked")
private void initGradient(FloatMLDataSet training, FloatMLDataSet testing, double[] weights, boolean isCrossOver) {
    int numLayers = (Integer) this.validParams.get(CommonConstants.NUM_HIDDEN_LAYERS);
    List<String> actFunc = (List<String>) this.validParams.get(CommonConstants.ACTIVATION_FUNC);
    List<Integer> hiddenNodeList = (List<Integer>) this.validParams.get(CommonConstants.NUM_HIDDEN_NODES);
    String outputActivationFunc = (String) validParams.get(CommonConstants.OUTPUT_ACTIVATION_FUNC);
    BasicNetwork network = DTrainUtils.generateNetwork(this.featureInputsCnt, this.outputNodeCount, numLayers, actFunc, hiddenNodeList, false, this.dropoutRate, this.wgtInit, CommonUtils.isLinearTarget(modelConfig, columnConfigList), outputActivationFunc);
    // use the weights from master
    network.getFlat().setWeights(weights);
    FlatNetwork flat = network.getFlat();
    // copy Propagation from encog, fix flat spot problem
    double[] flatSpot = new double[flat.getActivationFunctions().length];
    for (int i = 0; i < flat.getActivationFunctions().length; i++) {
        flatSpot[i] = flat.getActivationFunctions()[i] instanceof ActivationSigmoid ? 0.1 : 0.0;
    }
    LOG.info("Gradient computing thread count is {}.", modelConfig.getTrain().getWorkerThreadCount());
    this.gradient = new ParallelGradient((FloatFlatNetwork) flat, training, testing, flatSpot, new LinearErrorFunction(), isCrossOver, modelConfig.getTrain().getWorkerThreadCount(), this.lossStr, this.batchs);
}

Example 2 with LinearErrorFunction

use of org.encog.neural.error.LinearErrorFunction in project shifu by ShifuML.

the class DTrainTest method initGradient.

public Gradient initGradient(MLDataSet training) {
    FlatNetwork flat = network.getFlat().clone();
    // copy Propagation from encog
    double[] flatSpot = new double[flat.getActivationFunctions().length];
    for (int i = 0; i < flat.getActivationFunctions().length; i++) {
        final ActivationFunction af = flat.getActivationFunctions()[i];
        if (af instanceof ActivationSigmoid) {
            flatSpot[i] = 0.1;
        } else {
            flatSpot[i] = 0.0;
        }
    }
    return new Gradient(flat, training.openAdditional(), training, flatSpot, new LinearErrorFunction(), false);
}