Examples with DataSet org.nd4j.linalg.dataset.DataSet used on opensource projects

Example 46 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class TestDataSetIterator method next.

@Override
public DataSet next() {
    numDataSets++;
    DataSet next = wrapped.next();
    if (preProcessor != null)
        preProcessor.preProcess(next);
    return next;
}

Example 47 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class MagicQueue method poll.

/**
     * This method is supposed to be called from managed thread, attached to specific device.
     * It returns 1 DataSet element from head of the queue, and deletes that element from Queue.
     * If queue is empty,
     *
     * Please note: if there's nothing available in Queue - NULL will be returned
     * @param time time to wait for something appear in queue
     * @param timeUnit TimeUnit for time param
     * @return
     */
public DataSet poll(long time, TimeUnit timeUnit) throws InterruptedException {
    if (mode == Mode.THREADED) {
        if (numberOfBuckets > 1) {
            int deviceId = Nd4j.getAffinityManager().getDeviceForCurrentThread();
            DataSet ds = backingQueues.get(deviceId).poll(time, timeUnit);
            if (ds != null)
                cntGet.incrementAndGet();
            return ds;
        } else {
            DataSet ds = backingQueues.get(0).poll(time, timeUnit);
            if (ds != null)
                cntGet.incrementAndGet();
            return ds;
        }
    } else {
        //log.info("Trying queue_{}; queue_0: {}; queue_1: {}", interleavedCounter.get(), backingQueues.get(0).size(), backingQueues.get(1).size());
        DataSet ds = backingQueues.get(interleavedCounter.getAndIncrement()).poll(time, timeUnit);
        if (interleavedCounter.get() >= backingQueues.size())
            interleavedCounter.set(0);
        if (ds != null)
            cntGet.incrementAndGet();
        return ds;
    }
}

Example 48 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetIterator method getDataSet.

private DataSet getDataSet(List<Writable> record) {
    List<Writable> currList;
    if (record instanceof List)
        currList = record;
    else
        currList = new ArrayList<>(record);
    //allow people to specify label index as -1 and infer the last possible label
    if (numPossibleLabels >= 1 && labelIndex < 0) {
        labelIndex = record.size() - 1;
    }
    INDArray label = null;
    INDArray featureVector = null;
    int featureCount = 0;
    int labelCount = 0;
    //no labels
    if (currList.size() == 2 && currList.get(1) instanceof NDArrayWritable && currList.get(0) instanceof NDArrayWritable && currList.get(0) == currList.get(1)) {
        NDArrayWritable writable = (NDArrayWritable) currList.get(0);
        return new DataSet(writable.get(), writable.get());
    }
    if (currList.size() == 2 && currList.get(0) instanceof NDArrayWritable) {
        if (!regression) {
            label = FeatureUtil.toOutcomeVector((int) Double.parseDouble(currList.get(1).toString()), numPossibleLabels);
        } else {
            if (currList.get(1) instanceof NDArrayWritable) {
                label = ((NDArrayWritable) currList.get(1)).get();
            } else {
                label = Nd4j.scalar(currList.get(1).toDouble());
            }
        }
        NDArrayWritable ndArrayWritable = (NDArrayWritable) currList.get(0);
        featureVector = ndArrayWritable.get();
        return new DataSet(featureVector, label);
    }
    for (int j = 0; j < currList.size(); j++) {
        Writable current = currList.get(j);
        //ndarray writable is an insane slow down herecd
        if (!(current instanceof NDArrayWritable) && current.toString().isEmpty())
            continue;
        if (regression && j == labelIndex && j == labelIndexTo && current instanceof NDArrayWritable) {
            //Case: NDArrayWritable for the labels
            label = ((NDArrayWritable) current).get();
        } else if (regression && j >= labelIndex && j <= labelIndexTo) {
            //This is the multi-label regression case
            if (label == null)
                label = Nd4j.create(1, (labelIndexTo - labelIndex + 1));
            label.putScalar(labelCount++, current.toDouble());
        } else if (labelIndex >= 0 && j == labelIndex) {
            //single label case (classification, etc)
            if (converter != null)
                try {
                    current = converter.convert(current);
                } catch (WritableConverterException e) {
                    e.printStackTrace();
                }
            if (numPossibleLabels < 1)
                throw new IllegalStateException("Number of possible labels invalid, must be >= 1");
            if (regression) {
                label = Nd4j.scalar(current.toDouble());
            } else {
                int curr = current.toInt();
                if (curr < 0 || curr >= numPossibleLabels) {
                    throw new DL4JInvalidInputException("Invalid classification data: expect label value (at label index column = " + labelIndex + ") to be in range 0 to " + (numPossibleLabels - 1) + " inclusive (0 to numClasses-1, with numClasses=" + numPossibleLabels + "); got label value of " + current);
                }
                label = FeatureUtil.toOutcomeVector(curr, numPossibleLabels);
            }
        } else {
            try {
                double value = current.toDouble();
                if (featureVector == null) {
                    if (regression && labelIndex >= 0) {
                        //Handle the possibly multi-label regression case here:
                        int nLabels = labelIndexTo - labelIndex + 1;
                        featureVector = Nd4j.create(1, currList.size() - nLabels);
                    } else {
                        //Classification case, and also no-labels case
                        featureVector = Nd4j.create(labelIndex >= 0 ? currList.size() - 1 : currList.size());
                    }
                }
                featureVector.putScalar(featureCount++, value);
            } catch (UnsupportedOperationException e) {
                // This isn't a scalar, so check if we got an array already
                if (current instanceof NDArrayWritable) {
                    assert featureVector == null;
                    featureVector = ((NDArrayWritable) current).get();
                } else {
                    throw e;
                }
            }
        }
    }
    return new DataSet(featureVector, labelIndex >= 0 ? label : featureVector);
}

Example 49 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetIterator method totalOutcomes.

@Override
public int totalOutcomes() {
    if (last == null) {
        DataSet next = next();
        last = next;
        useCurrent = true;
        return next.numOutcomes();
    } else
        return last.numOutcomes();
}

Example 50 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetIterator method next.

@Override
public DataSet next(int num) {
    if (useCurrent) {
        useCurrent = false;
        if (preProcessor != null)
            preProcessor.preProcess(last);
        return last;
    }
    List<DataSet> dataSets = new ArrayList<>();
    List<RecordMetaData> meta = (collectMetaData ? new ArrayList<RecordMetaData>() : null);
    for (int i = 0; i < num; i++) {
        if (!hasNext())
            break;
        if (recordReader instanceof SequenceRecordReader) {
            if (sequenceIter == null || !sequenceIter.hasNext()) {
                List<List<Writable>> sequenceRecord = ((SequenceRecordReader) recordReader).sequenceRecord();
                sequenceIter = sequenceRecord.iterator();
            }
            List<Writable> record = sequenceIter.next();
            dataSets.add(getDataSet(record));
        } else {
            if (collectMetaData) {
                Record record = recordReader.nextRecord();
                dataSets.add(getDataSet(record.getRecord()));
                meta.add(record.getMetaData());
            } else {
                List<Writable> record = recordReader.next();
                dataSets.add(getDataSet(record));
            }
        }
    }
    batchNum++;
    if (dataSets.isEmpty())
        return new DataSet();
    DataSet ret = DataSet.merge(dataSets);
    if (collectMetaData) {
        ret.setExampleMetaData(meta);
    }
    last = ret;
    if (preProcessor != null)
        preProcessor.preProcess(ret);
    //Add label name values to dataset
    if (recordReader.getLabels() != null)
        ret.setLabelNames(recordReader.getLabels());
    return ret;
}