Examples with FloatFlatNetwork ml.shifu.shifu.core.dtrain.dataset.FloatFlatNetwork used on opensource projects

Example 1 with FloatFlatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.FloatFlatNetwork in project shifu by ShifuML.

the class SubGradient method calculateError.

/**
 * Calculate the error for this neural network. The error is calculated
 * using root-mean-square(RMS).
 *
 * @param ec
 *            The error computation logic
 * @return The error percentage.
 */
public final double calculateError(ErrorCalculation ec) {
    final double[] actual = new double[this.getNetwork().getOutputCount()];
    final FloatMLDataPair pair = BasicFloatMLDataPair.createPair(testing.getInputSize(), testing.getIdealSize());
    for (long i = testLow; i <= testHigh; i++) {
        synchronized (this.owner) {
            if (this.isCrossOver) {
                // 3:1 to select testing data set, tmp hard code, TODO fix hard code issue
                if ((i + seed) % 4 < 3) {
                    this.testing.getRecord(i, pair);
                } else {
                    long trainingSize = this.training.getRecordCount();
                    // it's ok to take data from all training set
                    if (i < trainingSize) {
                        this.training.getRecord(i, pair);
                    } else {
                        this.training.getRecord(i % trainingSize, pair);
                    }
                }
            } else {
                this.testing.getRecord(i, pair);
            }
        }
        ((FloatFlatNetwork) this.getNetwork()).compute(pair.getInputArray(), actual);
        // copy float idea array to double for api compatibility
        if (doubleIdeal == null) {
            doubleIdeal = new double[pair.getIdealArray().length];
        }
        for (int j = 0; j < doubleIdeal.length; j++) {
            doubleIdeal[j] = pair.getIdealArray()[j];
        }
        synchronized (ec) {
            ec.updateError(actual, doubleIdeal, pair.getSignificance());
        }
    }
    return -1;
}

Example 2 with FloatFlatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.FloatFlatNetwork 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);
}