Examples with Frame org.nd4j.parameterserver.distributed.messages.Frame used on opensource projects

Example 1 with Frame

use of org.nd4j.parameterserver.distributed.messages.Frame in project deeplearning4j by deeplearning4j.

the class PartitionTrainingFunction method trainAllAtOnce.

protected void trainAllAtOnce(List<Sequence<ShallowSequenceElement>> sequences) {
    Frame bigFrame = new Frame(BasicSequenceProvider.getInstance().getNextValue());
    for (Sequence<ShallowSequenceElement> sequence : sequences) {
        Frame frame = elementsLearningAlgorithm.frameSequence(sequence, new AtomicLong(119L), 25e-3f);
        bigFrame.stackMessages(frame.getMessages());
    }
    if (bigFrame.size() > 0)
        paramServer.execDistributed(bigFrame);
}

Example 2 with Frame

use of org.nd4j.parameterserver.distributed.messages.Frame in project deeplearning4j by deeplearning4j.

the class SparkCBOW method frameSequence.

@Override
public Frame<? extends TrainingMessage> frameSequence(Sequence<ShallowSequenceElement> sequence, AtomicLong nextRandom, double learningRate) {
    // FIXME: totalElementsCount should have real value
    if (vectorsConfiguration.getSampling() > 0)
        sequence = BaseSparkLearningAlgorithm.applySubsampling(sequence, nextRandom, 10L, vectorsConfiguration.getSampling());
    int currentWindow = vectorsConfiguration.getWindow();
    if (vectorsConfiguration.getVariableWindows() != null && vectorsConfiguration.getVariableWindows().length != 0) {
        currentWindow = vectorsConfiguration.getVariableWindows()[RandomUtils.nextInt(vectorsConfiguration.getVariableWindows().length)];
    }
    if (frame == null)
        synchronized (this) {
            if (frame == null)
                frame = new ThreadLocal<>();
        }
    if (frame.get() == null)
        frame.set(new Frame<CbowRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    for (int i = 0; i < sequence.getElements().size(); i++) {
        nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
        int b = (int) nextRandom.get() % currentWindow;
        int end = currentWindow * 2 + 1 - b;
        ShallowSequenceElement currentWord = sequence.getElementByIndex(i);
        List<Integer> intsList = new ArrayList<>();
        for (int a = b; a < end; a++) {
            if (a != currentWindow) {
                int c = i - currentWindow + a;
                if (c >= 0 && c < sequence.size()) {
                    ShallowSequenceElement lastWord = sequence.getElementByIndex(c);
                    intsList.add(lastWord.getIndex());
                }
            }
        }
        // just converting values to int
        int[] windowWords = new int[intsList.size()];
        for (int x = 0; x < windowWords.length; x++) {
            windowWords[x] = intsList.get(x);
        }
        if (windowWords.length < 1)
            continue;
        iterateSample(currentWord, windowWords, nextRandom, learningRate, false, 0, true, null);
    }
    Frame<CbowRequestMessage> currentFrame = frame.get();
    frame.set(new Frame<CbowRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    return currentFrame;
}

Example 3 with Frame

use of org.nd4j.parameterserver.distributed.messages.Frame in project deeplearning4j by deeplearning4j.

the class SparkSkipGram method frameSequence.

@Override
public Frame<? extends TrainingMessage> frameSequence(Sequence<ShallowSequenceElement> sequence, AtomicLong nextRandom, double learningRate) {
    // FIXME: totalElementsCount should have real value
    if (vectorsConfiguration.getSampling() > 0)
        sequence = BaseSparkLearningAlgorithm.applySubsampling(sequence, nextRandom, 10L, vectorsConfiguration.getSampling());
    int currentWindow = vectorsConfiguration.getWindow();
    if (vectorsConfiguration.getVariableWindows() != null && vectorsConfiguration.getVariableWindows().length != 0) {
        currentWindow = vectorsConfiguration.getVariableWindows()[RandomUtils.nextInt(vectorsConfiguration.getVariableWindows().length)];
    }
    if (frame == null)
        synchronized (this) {
            if (frame == null)
                frame = new ThreadLocal<>();
        }
    if (frame.get() == null)
        frame.set(new Frame<SkipGramRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    for (int i = 0; i < sequence.size(); i++) {
        nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
        ShallowSequenceElement word = sequence.getElementByIndex(i);
        if (word == null)
            continue;
        int b = (int) (nextRandom.get() % currentWindow);
        int end = currentWindow * 2 + 1 - b;
        for (int a = b; a < end; a++) {
            if (a != currentWindow) {
                int c = i - currentWindow + a;
                if (c >= 0 && c < sequence.size()) {
                    ShallowSequenceElement lastWord = sequence.getElementByIndex(c);
                    iterateSample(word, lastWord, nextRandom, learningRate);
                    nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
                }
            }
        }
    }
    // at this moment we should have something in ThreadLocal Frame, so we'll send it to VoidParameterServer for processing
    Frame<SkipGramRequestMessage> currentFrame = frame.get();
    frame.set(new Frame<SkipGramRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    return currentFrame;
}

Example 4 with Frame

use of org.nd4j.parameterserver.distributed.messages.Frame in project deeplearning4j by deeplearning4j.

the class SparkDM method frameSequence.

@Override
public Frame<? extends TrainingMessage> frameSequence(Sequence<ShallowSequenceElement> sequence, AtomicLong nextRandom, double learningRate) {
    if (vectorsConfiguration.getSampling() > 0)
        sequence = BaseSparkLearningAlgorithm.applySubsampling(sequence, nextRandom, 10L, vectorsConfiguration.getSampling());
    int currentWindow = vectorsConfiguration.getWindow();
    if (vectorsConfiguration.getVariableWindows() != null && vectorsConfiguration.getVariableWindows().length != 0) {
        currentWindow = vectorsConfiguration.getVariableWindows()[RandomUtils.nextInt(vectorsConfiguration.getVariableWindows().length)];
    }
    if (frame == null)
        synchronized (this) {
            if (frame == null)
                frame = new ThreadLocal<>();
        }
    if (frame.get() == null)
        frame.set(new Frame<CbowRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    for (int i = 0; i < sequence.getElements().size(); i++) {
        nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
        int b = (int) nextRandom.get() % currentWindow;
        int end = currentWindow * 2 + 1 - b;
        ShallowSequenceElement currentWord = sequence.getElementByIndex(i);
        List<Integer> intsList = new ArrayList<>();
        for (int a = b; a < end; a++) {
            if (a != currentWindow) {
                int c = i - currentWindow + a;
                if (c >= 0 && c < sequence.size()) {
                    ShallowSequenceElement lastWord = sequence.getElementByIndex(c);
                    intsList.add(lastWord.getIndex());
                }
            }
        }
        // basically it's the same as CBOW, we just add labels here
        if (sequence.getSequenceLabels() != null) {
            for (ShallowSequenceElement label : sequence.getSequenceLabels()) {
                intsList.add(label.getIndex());
            }
        } else
            // FIXME: we probably should throw this exception earlier?
            throw new DL4JInvalidInputException("Sequence passed via RDD has no labels within, nothing to learn here");
        // just converting values to int
        int[] windowWords = new int[intsList.size()];
        for (int x = 0; x < windowWords.length; x++) {
            windowWords[x] = intsList.get(x);
        }
        if (windowWords.length < 1)
            continue;
        iterateSample(currentWord, windowWords, nextRandom, learningRate, false, 0, true, null);
    }
    Frame<CbowRequestMessage> currentFrame = frame.get();
    frame.set(new Frame<CbowRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    return currentFrame;
}

Example 5 with Frame

use of org.nd4j.parameterserver.distributed.messages.Frame in project deeplearning4j by deeplearning4j.

the class SparkDBOW method frameSequence.

@Override
public Frame<? extends TrainingMessage> frameSequence(Sequence<ShallowSequenceElement> sequence, AtomicLong nextRandom, double learningRate) {
    if (vectorsConfiguration.getSampling() > 0)
        sequence = BaseSparkLearningAlgorithm.applySubsampling(sequence, nextRandom, 10L, vectorsConfiguration.getSampling());
    int currentWindow = vectorsConfiguration.getWindow();
    if (vectorsConfiguration.getVariableWindows() != null && vectorsConfiguration.getVariableWindows().length != 0) {
        currentWindow = vectorsConfiguration.getVariableWindows()[RandomUtils.nextInt(vectorsConfiguration.getVariableWindows().length)];
    }
    if (frame == null)
        synchronized (this) {
            if (frame == null)
                frame = new ThreadLocal<>();
        }
    if (frame.get() == null)
        frame.set(new Frame<SkipGramRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    for (ShallowSequenceElement lastWord : sequence.getSequenceLabels()) {
        for (ShallowSequenceElement word : sequence.getElements()) {
            iterateSample(word, lastWord, nextRandom, learningRate);
            nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
        }
    }
    // at this moment we should have something in ThreadLocal Frame, so we'll send it to VoidParameterServer for processing
    Frame<SkipGramRequestMessage> currentFrame = frame.get();
    frame.set(new Frame<SkipGramRequestMessage>(BasicSequenceProvider.getInstance().getNextValue()));
    return currentFrame;
}