use of org.nd4j.parameterserver.distributed.messages.requests.CbowRequestMessage 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;
}
use of org.nd4j.parameterserver.distributed.messages.requests.CbowRequestMessage 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;
}
use of org.nd4j.parameterserver.distributed.messages.requests.CbowRequestMessage in project deeplearning4j by deeplearning4j.
the class SparkCBOW method iterateSample.
protected void iterateSample(ShallowSequenceElement currentWord, int[] windowWords, AtomicLong nextRandom, double alpha, boolean isInference, int numLabels, boolean trainWords, INDArray inferenceVector) {
int[] idxSyn1 = null;
byte[] codes = null;
if (vectorsConfiguration.isUseHierarchicSoftmax()) {
idxSyn1 = new int[currentWord.getCodeLength()];
codes = new byte[currentWord.getCodeLength()];
for (int p = 0; p < currentWord.getCodeLength(); p++) {
if (currentWord.getPoints().get(p) < 0)
continue;
codes[p] = currentWord.getCodes().get(p);
idxSyn1[p] = currentWord.getPoints().get(p);
}
} else {
idxSyn1 = new int[0];
codes = new byte[0];
}
CbowRequestMessage cbrm = new CbowRequestMessage(windowWords, idxSyn1, currentWord.getIndex(), codes, (int) vectorsConfiguration.getNegative(), alpha, nextRandom.get());
frame.get().stackMessage(cbrm);
}
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