Examples with NNParams ml.shifu.shifu.core.dtrain.nn.NNParams used on opensource projects

Example 1 with NNParams

use of ml.shifu.shifu.core.dtrain.nn.NNParams in project shifu by ShifuML.

the class DTrainTest method manhantTest.

@Test
public void manhantTest() throws IOException {
    double[] gradientError = new double[NUM_EPOCHS];
    double[] ecogError = new double[NUM_EPOCHS];
    network.reset();
    weights = network.getFlat().getWeights();
    MLDataSet[] subsets = splitDataSet(training);
    Gradient[] workers = new Gradient[numSplit];
    Weight weightCalculator = null;
    for (int i = 0; i < workers.length; i++) {
        workers[i] = initGradient(subsets[i]);
        workers[i].setWeights(weights);
    }
    NNParams globalParams = new NNParams();
    globalParams.setWeights(weights);
    log.info("Starting manhattan propagation testing!");
    for (int i = 0; i < NUM_EPOCHS; i++) {
        double error = 0.0;
        // each worker do the job
        for (int j = 0; j < workers.length; j++) {
            workers[j].run();
            error += workers[j].getError();
        }
        gradientError[i] = error / workers.length;
        log.info("The #" + i + " training error: " + gradientError[i]);
        // master
        globalParams.reset();
        for (int j = 0; j < workers.length; j++) {
            globalParams.accumulateGradients(workers[j].getGradients());
            globalParams.accumulateTrainSize(subsets[j].getRecordCount());
        }
        if (weightCalculator == null) {
            weightCalculator = new Weight(globalParams.getGradients().length, globalParams.getTrainSize(), this.rate, DTrainUtils.MANHATTAN_PROPAGATION, 0, RegulationLevel.NONE);
        }
        double[] interWeight = weightCalculator.calculateWeights(globalParams.getWeights(), globalParams.getGradients(), -1);
        globalParams.setWeights(interWeight);
        // set weights
        for (int j = 0; j < workers.length; j++) {
            workers[j].setWeights(interWeight);
        }
    }
    // encog
    network.reset();
    // NNUtils.randomize(numSplit, weights);
    network.getFlat().setWeights(weights);
    Propagation p = null;
    p = new ManhattanPropagation(network, training, rate);
    p.setThreadCount(numSplit);
    for (int i = 0; i < NUM_EPOCHS; i++) {
        p.iteration(1);
        // System.out.println("the #" + i + " training error: " + p.getError());
        ecogError[i] = p.getError();
    }
    // assert
    double diff = 0.0;
    for (int i = 0; i < NUM_EPOCHS; i++) {
        diff += Math.abs(ecogError[i] - gradientError[i]);
    }
    Assert.assertTrue(diff / NUM_EPOCHS < 0.3);
}

Example 2 with NNParams

use of ml.shifu.shifu.core.dtrain.nn.NNParams in project shifu by ShifuML.

the class DTrainTest method resilientPropagationTest.

@Test
public void resilientPropagationTest() {
    double[] gradientError = new double[NUM_EPOCHS];
    double[] ecogError = new double[NUM_EPOCHS];
    network.reset();
    weights = network.getFlat().getWeights();
    MLDataSet[] subsets = splitDataSet(training);
    Gradient[] workers = new Gradient[numSplit];
    Weight weightCalculator = null;
    for (int i = 0; i < workers.length; i++) {
        workers[i] = initGradient(subsets[i]);
        workers[i].setWeights(weights);
    }
    log.info("Starting resilient propagation testing!");
    NNParams globalParams = new NNParams();
    globalParams.setWeights(weights);
    for (int i = 0; i < NUM_EPOCHS; i++) {
        double error = 0.0;
        // each worker do the job
        for (int j = 0; j < workers.length; j++) {
            workers[j].run();
            error += workers[j].getError();
        }
        gradientError[i] = error / workers.length;
        log.info("The #" + i + " training error: " + gradientError[i]);
        // master
        globalParams.reset();
        for (int j = 0; j < workers.length; j++) {
            globalParams.accumulateGradients(workers[j].getGradients());
            globalParams.accumulateTrainSize(subsets[j].getRecordCount());
        }
        if (weightCalculator == null) {
            weightCalculator = new Weight(globalParams.getGradients().length, globalParams.getTrainSize(), this.rate, DTrainUtils.RESILIENTPROPAGATION, 0, RegulationLevel.NONE);
        }
        double[] interWeight = weightCalculator.calculateWeights(globalParams.getWeights(), globalParams.getGradients(), -1);
        globalParams.setWeights(interWeight);
        // set weights
        for (int j = 0; j < workers.length; j++) {
            workers[j].setWeights(interWeight);
        }
    }
    // encog
    network.reset();
    // NNUtils.randomize(numSplit, weights);
    network.getFlat().setWeights(weights);
    Propagation p = null;
    p = new ResilientPropagation(network, training);
    p.setThreadCount(numSplit);
    for (int i = 0; i < NUM_EPOCHS; i++) {
        p.iteration(1);
        ecogError[i] = p.getError();
    }
    // assert
    double diff = 0.0;
    for (int i = 0; i < NUM_EPOCHS; i++) {
        diff += Math.abs(ecogError[i] - gradientError[i]);
    }
    Assert.assertTrue(diff / NUM_EPOCHS < 0.2);
}

Example 3 with NNParams

use of ml.shifu.shifu.core.dtrain.nn.NNParams in project shifu by ShifuML.

the class DTrainTest method quickTest.

@Test
public void quickTest() throws IOException {
    double[] gradientError = new double[NUM_EPOCHS];
    double[] ecogError = new double[NUM_EPOCHS];
    network.reset();
    weights = network.getFlat().getWeights();
    MLDataSet[] subsets = splitDataSet(training);
    Gradient[] workers = new Gradient[numSplit];
    Weight weightCalculator = null;
    for (int i = 0; i < workers.length; i++) {
        workers[i] = initGradient(subsets[i]);
        workers[i].setWeights(weights);
    }
    log.info("Running QuickPropagtaion testing! ");
    NNParams globalParams = new NNParams();
    globalParams.setWeights(weights);
    for (int i = 0; i < NUM_EPOCHS; i++) {
        double error = 0.0;
        // each worker do the job
        for (int j = 0; j < workers.length; j++) {
            workers[j].run();
            error += workers[j].getError();
        }
        gradientError[i] = error / workers.length;
        log.info("The #" + i + " training error: " + gradientError[i]);
        // master
        globalParams.reset();
        for (int j = 0; j < workers.length; j++) {
            globalParams.accumulateGradients(workers[j].getGradients());
            globalParams.accumulateTrainSize(subsets[j].getRecordCount());
        }
        if (weightCalculator == null) {
            weightCalculator = new Weight(globalParams.getGradients().length, globalParams.getTrainSize(), this.rate, DTrainUtils.QUICK_PROPAGATION, 0, RegulationLevel.NONE);
        }
        double[] interWeight = weightCalculator.calculateWeights(globalParams.getWeights(), globalParams.getGradients(), -1);
        globalParams.setWeights(interWeight);
        // set weights
        for (int j = 0; j < workers.length; j++) {
            workers[j].setWeights(interWeight);
        }
    }
    // encog
    network.reset();
    // NNUtils.randomize(numSplit, weights);
    network.getFlat().setWeights(weights);
    Propagation p = null;
    p = new QuickPropagation(network, training, rate);
    // p = new ManhattanPropagation(network, training, rate);
    p.setThreadCount(numSplit);
    for (int i = 0; i < NUM_EPOCHS; i++) {
        p.iteration(1);
        // System.out.println("the #" + i + " training error: " + p.getError());
        ecogError[i] = p.getError();
    }
    // assert
    double diff = 0.0;
    for (int i = 0; i < NUM_EPOCHS; i++) {
        diff += Math.abs(ecogError[i] - gradientError[i]);
    }
    Assert.assertTrue(diff / NUM_EPOCHS < 0.1);
}

Example 4 with NNParams

use of ml.shifu.shifu.core.dtrain.nn.NNParams in project shifu by ShifuML.

the class DTrainTest method backTest.

@Test
public void backTest() {
    double[] gradientError = new double[NUM_EPOCHS];
    double[] ecogError = new double[NUM_EPOCHS];
    network.reset();
    weights = network.getFlat().getWeights();
    MLDataSet[] subsets = splitDataSet(training);
    Gradient[] workers = new Gradient[numSplit];
    Weight weightCalculator = null;
    for (int i = 0; i < workers.length; i++) {
        workers[i] = initGradient(subsets[i]);
        workers[i].setWeights(weights);
    }
    log.info("Starting back propagation testing!");
    NNParams globalParams = new NNParams();
    globalParams.setWeights(weights);
    for (int i = 0; i < NUM_EPOCHS; i++) {
        double error = 0.0;
        // each worker do the job
        for (int j = 0; j < workers.length; j++) {
            workers[j].run();
            error += workers[j].getError();
        }
        gradientError[i] = error / workers.length;
        log.info("The #" + i + " training error: " + gradientError[i]);
        // master
        globalParams.reset();
        for (int j = 0; j < workers.length; j++) {
            globalParams.accumulateGradients(workers[j].getGradients());
            globalParams.accumulateTrainSize(subsets[j].getRecordCount());
        }
        if (weightCalculator == null) {
            weightCalculator = new Weight(globalParams.getGradients().length, globalParams.getTrainSize(), this.rate, DTrainUtils.BACK_PROPAGATION, 0, RegulationLevel.NONE);
        }
        double[] interWeight = weightCalculator.calculateWeights(globalParams.getWeights(), globalParams.getGradients(), -1);
        globalParams.setWeights(interWeight);
        // set weights
        for (int j = 0; j < workers.length; j++) {
            workers[j].setWeights(interWeight);
        }
    }
    // encog
    network.reset();
    // NNUtils.randomize(numSplit, weights);
    network.getFlat().setWeights(weights);
    Propagation p = null;
    p = new Backpropagation(network, training, rate, 0.5);
    p.setThreadCount(numSplit);
    for (int i = 0; i < NUM_EPOCHS; i++) {
        p.iteration(1);
        // System.out.println("the #" + i + " training error: " + p.getError());
        ecogError[i] = p.getError();
    }
    // assert
    double diff = 0.0;
    for (int i = 0; i < NUM_EPOCHS; i++) {
        diff += Math.abs(ecogError[i] - gradientError[i]);
    }
    Assert.assertTrue(diff / NUM_EPOCHS < 0.2);
}