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128,700 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java | ImageLoader.toRaveledTensor | public INDArray toRaveledTensor(BufferedImage image) {
try {
image = scalingIfNeed(image, false);
return toINDArrayBGR(image).ravel();
} catch (Exception e) {
throw new RuntimeException("Unable to load image", e);
}
} | java | public INDArray toRaveledTensor(BufferedImage image) {
try {
image = scalingIfNeed(image, false);
return toINDArrayBGR(image).ravel();
} catch (Exception e) {
throw new RuntimeException("Unable to load image", e);
}
} | [
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128,701 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java | ImageLoader.toBgr | public INDArray toBgr(BufferedImage image) {
if (image == null)
throw new IllegalStateException("Unable to load image");
image = scalingIfNeed(image, false);
return toINDArrayBGR(image);
} | java | public INDArray toBgr(BufferedImage image) {
if (image == null)
throw new IllegalStateException("Unable to load image");
image = scalingIfNeed(image, false);
return toINDArrayBGR(image);
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128,702 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java | ImageLoader.asMatrix | public INDArray asMatrix(InputStream inputStream) throws IOException {
if (channels == 3)
return toBgr(inputStream);
try {
BufferedImage image = ImageIO.read(inputStream);
return asMatrix(image);
} catch (IOException e) {
throw new IOException("Una... | java | public INDArray asMatrix(InputStream inputStream) throws IOException {
if (channels == 3)
return toBgr(inputStream);
try {
BufferedImage image = ImageIO.read(inputStream);
return asMatrix(image);
} catch (IOException e) {
throw new IOException("Una... | [
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128,703 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java | ImageLoader.asMatrix | public INDArray asMatrix(BufferedImage image) {
if (channels == 3) {
return toBgr(image);
} else {
image = scalingIfNeed(image, true);
int w = image.getWidth();
int h = image.getHeight();
INDArray ret = Nd4j.create(h, w);
for (int ... | java | public INDArray asMatrix(BufferedImage image) {
if (channels == 3) {
return toBgr(image);
} else {
image = scalingIfNeed(image, true);
int w = image.getWidth();
int h = image.getHeight();
INDArray ret = Nd4j.create(h, w);
for (int ... | [
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128,704 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java | ImageLoader.asImageMiniBatches | public INDArray asImageMiniBatches(File f, int numMiniBatches, int numRowsPerSlice) {
try {
INDArray d = asMatrix(f);
return Nd4j.create(numMiniBatches, numRowsPerSlice, d.columns());
} catch (Exception e) {
throw new RuntimeException(e);
}
} | java | public INDArray asImageMiniBatches(File f, int numMiniBatches, int numRowsPerSlice) {
try {
INDArray d = asMatrix(f);
return Nd4j.create(numMiniBatches, numRowsPerSlice, d.columns());
} catch (Exception e) {
throw new RuntimeException(e);
}
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128,705 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java | ImageLoader.toImage | public static BufferedImage toImage(INDArray matrix) {
BufferedImage img = new BufferedImage(matrix.rows(), matrix.columns(), BufferedImage.TYPE_INT_ARGB);
WritableRaster r = img.getRaster();
int[] equiv = new int[(int) matrix.length()];
for (int i = 0; i < equiv.length; i++) {
... | java | public static BufferedImage toImage(INDArray matrix) {
BufferedImage img = new BufferedImage(matrix.rows(), matrix.columns(), BufferedImage.TYPE_INT_ARGB);
WritableRaster r = img.getRaster();
int[] equiv = new int[(int) matrix.length()];
for (int i = 0; i < equiv.length; i++) {
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128,706 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.mean | public static Column mean(DataRowsFacade dataFrame, String columnName) {
return dataFrame.get().groupBy(columnName).agg(avg(columnName)).col(columnName);
} | java | public static Column mean(DataRowsFacade dataFrame, String columnName) {
return dataFrame.get().groupBy(columnName).agg(avg(columnName)).col(columnName);
} | [
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128,707 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.fromSchema | public static StructType fromSchema(Schema schema) {
StructField[] structFields = new StructField[schema.numColumns()];
for (int i = 0; i < structFields.length; i++) {
switch (schema.getColumnTypes().get(i)) {
case Double:
structFields[i] = new StructField... | java | public static StructType fromSchema(Schema schema) {
StructField[] structFields = new StructField[schema.numColumns()];
for (int i = 0; i < structFields.length; i++) {
switch (schema.getColumnTypes().get(i)) {
case Double:
structFields[i] = new StructField... | [
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128,708 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.fromStructType | public static Schema fromStructType(StructType structType) {
Schema.Builder builder = new Schema.Builder();
StructField[] fields = structType.fields();
String[] fieldNames = structType.fieldNames();
for (int i = 0; i < fields.length; i++) {
String name = fields[i].dataType().... | java | public static Schema fromStructType(StructType structType) {
Schema.Builder builder = new Schema.Builder();
StructField[] fields = structType.fields();
String[] fieldNames = structType.fieldNames();
for (int i = 0; i < fields.length; i++) {
String name = fields[i].dataType().... | [
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128,709 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.toRecords | public static Pair<Schema, JavaRDD<List<Writable>>> toRecords(DataRowsFacade dataFrame) {
Schema schema = fromStructType(dataFrame.get().schema());
return new Pair<>(schema, dataFrame.get().javaRDD().map(new ToRecord(schema)));
} | java | public static Pair<Schema, JavaRDD<List<Writable>>> toRecords(DataRowsFacade dataFrame) {
Schema schema = fromStructType(dataFrame.get().schema());
return new Pair<>(schema, dataFrame.get().javaRDD().map(new ToRecord(schema)));
} | [
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128,710 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.toDataFrame | public static DataRowsFacade toDataFrame(Schema schema, JavaRDD<List<Writable>> data) {
JavaSparkContext sc = new JavaSparkContext(data.context());
SQLContext sqlContext = new SQLContext(sc);
JavaRDD<Row> rows = data.map(new ToRow(schema));
return dataRows(sqlContext.createDataFrame(rows... | java | public static DataRowsFacade toDataFrame(Schema schema, JavaRDD<List<Writable>> data) {
JavaSparkContext sc = new JavaSparkContext(data.context());
SQLContext sqlContext = new SQLContext(sc);
JavaRDD<Row> rows = data.map(new ToRow(schema));
return dataRows(sqlContext.createDataFrame(rows... | [
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128,711 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.rowToWritables | public static List<Writable> rowToWritables(Schema schema, Row row) {
List<Writable> ret = new ArrayList<>();
for (int i = 0; i < row.size(); i++) {
switch (schema.getType(i)) {
case Double:
ret.add(new DoubleWritable(row.getDouble(i)));
... | java | public static List<Writable> rowToWritables(Schema schema, Row row) {
List<Writable> ret = new ArrayList<>();
for (int i = 0; i < row.size(); i++) {
switch (schema.getType(i)) {
case Double:
ret.add(new DoubleWritable(row.getDouble(i)));
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128,712 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.toList | public static List<String> toList(String[] input) {
List<String> ret = new ArrayList<>();
for (int i = 0; i < input.length; i++)
ret.add(input[i]);
return ret;
} | java | public static List<String> toList(String[] input) {
List<String> ret = new ArrayList<>();
for (int i = 0; i < input.length; i++)
ret.add(input[i]);
return ret;
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128,713 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.toArray | public static String[] toArray(List<String> list) {
String[] ret = new String[list.size()];
for (int i = 0; i < ret.length; i++)
ret[i] = list.get(i);
return ret;
} | java | public static String[] toArray(List<String> list) {
String[] ret = new String[list.size()];
for (int i = 0; i < ret.length; i++)
ret[i] = list.get(i);
return ret;
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128,714 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.toMatrix | public static INDArray toMatrix(List<Row> rows) {
INDArray ret = Nd4j.create(rows.size(), rows.get(0).size());
for (int i = 0; i < ret.rows(); i++) {
for (int j = 0; j < ret.columns(); j++) {
ret.putScalar(i, j, rows.get(i).getDouble(j));
}
}
retur... | java | public static INDArray toMatrix(List<Row> rows) {
INDArray ret = Nd4j.create(rows.size(), rows.get(0).size());
for (int i = 0; i < ret.rows(); i++) {
for (int j = 0; j < ret.columns(); j++) {
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128,715 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.toColumn | public static List<Column> toColumn(List<String> columns) {
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128,716 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/DataFrames.java | DataFrames.toColumns | public static Column[] toColumns(String... columns) {
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128,718 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/main/java/org/deeplearning4j/spark/time/NTPTimeSource.java | NTPTimeSource.getUpdateFrequencyConfiguration | private static long getUpdateFrequencyConfiguration() {
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long updateFreq;
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String property = System.getProperty(DL4JSystemProperties.NTP_SOURCE_UPDATE_FREQUENCY_MS_PROPERTY);
Long parseAttempt = null;
long updateFreq;
if (property != null) {
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128,719 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dimensionalityreduction/RandomProjection.java | RandomProjection.gaussianRandomMatrix | private INDArray gaussianRandomMatrix(long[] shape, Random rng){
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return res;
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Nd4j.checkShapeValues(shape);
INDArray res = Nd4j.create(shape);
GaussianDistribution op1 = new GaussianDistribution(res, 0.0, 1.0 / Math.sqrt(shape[0]));
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128,720 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java | KerasBatchNormalization.getEpsFromConfig | private double getEpsFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
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throw new InvalidKerasConfigurat... | java | private double getEpsFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
if (!innerConfig.containsKey(LAYER_FIELD_EPSILON))
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128,721 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java | KerasBatchNormalization.getMomentumFromConfig | private double getMomentumFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
if (!innerConfig.containsKey(LAYER_FIELD_MOMENTUM))
throw new InvalidKerasConf... | java | private double getMomentumFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
if (!innerConfig.containsKey(LAYER_FIELD_MOMENTUM))
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128,722 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java | KerasBatchNormalization.getGammaRegularizerFromConfig | private void getGammaRegularizerFromConfig(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws UnsupportedKerasConfigurationException, InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
i... | java | private void getGammaRegularizerFromConfig(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws UnsupportedKerasConfigurationException, InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
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128,723 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java | KerasBatchNormalization.getBetaRegularizerFromConfig | private void getBetaRegularizerFromConfig(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws UnsupportedKerasConfigurationException, InvalidKerasConfigurationException {
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if... | java | private void getBetaRegularizerFromConfig(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
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int batchNormMo... | java | private int getBatchNormMode(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
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128,725 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java | KerasBatchNormalization.getBatchNormAxis | private int getBatchNormAxis(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
return (int) innerConfig.get(LAYER_FIELD_AXIS);
} | java | private int getBatchNormAxis(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
return (int) innerConfig.get(LAYER_FIELD_AXIS);
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128,726 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/writable/IntWritable.java | IntWritable.compareTo | public int compareTo(Object o) {
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int thatValue = ((IntWritable) o).value;
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128,727 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/iterators/AbstractSequenceIterator.java | AbstractSequenceIterator.nextSequence | @Override
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return sequence;
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128,728 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/Convolution1DUtils.java | Convolution1DUtils.validateConvolutionModePadding | public static void validateConvolutionModePadding(ConvolutionMode mode, int padding) {
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128,729 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/Convolution1DUtils.java | Convolution1DUtils.getSameModeTopLeftPadding | public static int getSameModeTopLeftPadding(int outSize, int inSize, int kernel, int strides, int dilation) {
int eKernel = effectiveKernelSize(kernel, dilation);
//Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
int outPad = ((outSize - 1) * strides + eKernel -... | java | public static int getSameModeTopLeftPadding(int outSize, int inSize, int kernel, int strides, int dilation) {
int eKernel = effectiveKernelSize(kernel, dilation);
//Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
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128,730 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/listeners/SerializingListener.java | SerializingListener.validateEvent | @Override
public boolean validateEvent(ListenerEvent event, long argument) {
try {
/**
* please note, since sequence vectors are multithreaded we need to stop processed while model is being saved
*/
locker.acquire();
if (event == targetEvent && ... | java | @Override
public boolean validateEvent(ListenerEvent event, long argument) {
try {
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* please note, since sequence vectors are multithreaded we need to stop processed while model is being saved
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128,731 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/listeners/SerializingListener.java | SerializingListener.processEvent | @Override
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try {
locker.acquire();
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StringBuilder builder = new StringBuilder(targetFolder.getAbsolut... | java | @Override
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try {
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128,732 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/serde/base64/Nd4jBase64.java | Nd4jBase64.arraysFromBase64 | public static INDArray[] arraysFromBase64(String base64) throws IOException {
String[] base64Arr = base64.split("\t");
INDArray[] ret = new INDArray[base64Arr.length];
for (int i = 0; i < base64Arr.length; i++) {
byte[] decode = Base64.decodeBase64(base64Arr[i]);
ByteArra... | java | public static INDArray[] arraysFromBase64(String base64) throws IOException {
String[] base64Arr = base64.split("\t");
INDArray[] ret = new INDArray[base64Arr.length];
for (int i = 0; i < base64Arr.length; i++) {
byte[] decode = Base64.decodeBase64(base64Arr[i]);
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128,733 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/serde/base64/Nd4jBase64.java | Nd4jBase64.arraysToBase64 | public static String arraysToBase64(INDArray[] arrays) throws IOException {
StringBuilder sb = new StringBuilder();
//tab separate the outputs for de serialization
for (INDArray outputArr : arrays) {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
DataOutputStrea... | java | public static String arraysToBase64(INDArray[] arrays) throws IOException {
StringBuilder sb = new StringBuilder();
//tab separate the outputs for de serialization
for (INDArray outputArr : arrays) {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
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128,734 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/serde/base64/Nd4jBase64.java | Nd4jBase64.base64String | public static String base64String(INDArray arr) throws IOException {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
DataOutputStream dos = new DataOutputStream(bos);
Nd4j.write(arr, dos);
String base64 = Base64.encodeBase64String(bos.toByteArray());
return base64;
} | java | public static String base64String(INDArray arr) throws IOException {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
DataOutputStream dos = new DataOutputStream(bos);
Nd4j.write(arr, dos);
String base64 = Base64.encodeBase64String(bos.toByteArray());
return base64;
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128,735 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/serde/base64/Nd4jBase64.java | Nd4jBase64.fromBase64 | public static INDArray fromBase64(String base64) throws IOException {
byte[] arr = Base64.decodeBase64(base64);
ByteArrayInputStream bis = new ByteArrayInputStream(arr);
DataInputStream dis = new DataInputStream(bis);
INDArray predict = Nd4j.read(dis);
return predict;
} | java | public static INDArray fromBase64(String base64) throws IOException {
byte[] arr = Base64.decodeBase64(base64);
ByteArrayInputStream bis = new ByteArrayInputStream(arr);
DataInputStream dis = new DataInputStream(bis);
INDArray predict = Nd4j.read(dis);
return predict;
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128,736 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/RandomMultiDataSetIterator.java | RandomMultiDataSetIterator.generate | public static INDArray generate(long[] shape, Values values) {
return generate(shape, Nd4j.order(), values);
} | java | public static INDArray generate(long[] shape, Values values) {
return generate(shape, Nd4j.order(), values);
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128,737 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/params/ConvolutionParamInitializer.java | ConvolutionParamInitializer.createBias | protected INDArray createBias(NeuralNetConfiguration conf, INDArray biasView, boolean initializeParams) {
//the bias is a 1D tensor -- one bias per output feature map
org.deeplearning4j.nn.conf.layers.ConvolutionLayer layerConf =
(org.deeplearning4j.nn.conf.layers.ConvolutionLaye... | java | protected INDArray createBias(NeuralNetConfiguration conf, INDArray biasView, boolean initializeParams) {
//the bias is a 1D tensor -- one bias per output feature map
org.deeplearning4j.nn.conf.layers.ConvolutionLayer layerConf =
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128,738 | deeplearning4j/deeplearning4j | nd4j/nd4j-parameter-server-parent/nd4j-parameter-server/src/main/java/org/nd4j/parameterserver/updater/SynchronousParameterUpdater.java | SynchronousParameterUpdater.status | @Override
public Map<String, Number> status() {
Map<String, Number> ret = new HashMap<>();
ret.put("workers", workers);
ret.put("accumulatedUpdates", numUpdates());
return ret;
} | java | @Override
public Map<String, Number> status() {
Map<String, Number> ret = new HashMap<>();
ret.put("workers", workers);
ret.put("accumulatedUpdates", numUpdates());
return ret;
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128,739 | deeplearning4j/deeplearning4j | nd4j/nd4j-parameter-server-parent/nd4j-parameter-server/src/main/java/org/nd4j/parameterserver/updater/SynchronousParameterUpdater.java | SynchronousParameterUpdater.toJson | @Override
public String toJson() {
try {
return objectMapper.writeValueAsString(status());
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
} | java | @Override
public String toJson() {
try {
return objectMapper.writeValueAsString(status());
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
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128,740 | deeplearning4j/deeplearning4j | nd4j/nd4j-parameter-server-parent/nd4j-parameter-server/src/main/java/org/nd4j/parameterserver/updater/SynchronousParameterUpdater.java | SynchronousParameterUpdater.update | @Override
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updateStorage.addUpdate(message);
INDArray arr = message.getArr();
//of note for ndarrays
int[] dimensions = message.getDimensions();
boolean whole = dimensions.length == 1 && dimensions[0] == -1;
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... | java | @Override
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updateStorage.addUpdate(message);
INDArray arr = message.getArr();
//of note for ndarrays
int[] dimensions = message.getDimensions();
boolean whole = dimensions.length == 1 && dimensions[0] == -1;
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128,741 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/text/documentiterator/SimpleLabelAwareIterator.java | SimpleLabelAwareIterator.nextDocument | @Override
public LabelledDocument nextDocument() {
LabelledDocument document = currentIterator.next();
for (String label : document.getLabels()) {
labels.storeLabel(label);
}
return document;
} | java | @Override
public LabelledDocument nextDocument() {
LabelledDocument document = currentIterator.next();
for (String label : document.getLabels()) {
labels.storeLabel(label);
}
return document;
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128,742 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-ui/src/main/java/org/deeplearning4j/ui/WebReporter.java | WebReporter.queueReport | public void queueReport(WebTarget target, Entity entity) {
queue.add(Pair.makePair(target, entity));
} | java | public void queueReport(WebTarget target, Entity entity) {
queue.add(Pair.makePair(target, entity));
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128,743 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-ui/src/main/java/org/deeplearning4j/ui/WebReporter.java | WebReporter.postReport | public void postReport(WebTarget target, Entity entity) {
Response resp = target.request(MediaType.APPLICATION_JSON).accept(MediaType.APPLICATION_JSON).post(entity);
log.debug("{}", resp);
} | java | public void postReport(WebTarget target, Entity entity) {
Response resp = target.request(MediaType.APPLICATION_JSON).accept(MediaType.APPLICATION_JSON).post(entity);
log.debug("{}", resp);
} | [
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128,744 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/collection/MultiDimensionalMap.java | MultiDimensionalMap.newThreadSafeTreeBackedMap | public static <K, T, V> MultiDimensionalMap<K, T, V> newThreadSafeTreeBackedMap() {
return new MultiDimensionalMap<>(new ConcurrentSkipListMap<Pair<K, T>, V>());
} | java | public static <K, T, V> MultiDimensionalMap<K, T, V> newThreadSafeTreeBackedMap() {
return new MultiDimensionalMap<>(new ConcurrentSkipListMap<Pair<K, T>, V>());
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128,745 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/collection/MultiDimensionalMap.java | MultiDimensionalMap.newThreadSafeHashBackedMap | public static <K, T, V> MultiDimensionalMap<K, T, V> newThreadSafeHashBackedMap() {
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128,746 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/collection/MultiDimensionalMap.java | MultiDimensionalMap.newHashBackedMap | public static <K, T, V> MultiDimensionalMap<K, T, V> newHashBackedMap() {
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} | java | public static <K, T, V> MultiDimensionalMap<K, T, V> newHashBackedMap() {
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128,747 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/collection/MultiDimensionalMap.java | MultiDimensionalMap.newTreeBackedMap | public static <K, T, V> MultiDimensionalMap<K, T, V> newTreeBackedMap() {
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} | java | public static <K, T, V> MultiDimensionalMap<K, T, V> newTreeBackedMap() {
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128,748 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/stats/MinMaxStats.java | MinMaxStats.getRange | public INDArray getRange() {
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range = upper.sub(lower);
}
}
return range;
} | java | public INDArray getRange() {
if (range == null) {
try (MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
range = upper.sub(lower);
}
}
return range;
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128,749 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/CpuNDArrayFactory.java | CpuNDArrayFactory.average | @Override
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if (arrays == null || arrays.length == 0)
throw new RuntimeException("Input arrays are missing");
if (arrays.length == 1) {
//Edge case - average 1 array - no op
if(target == null){
... | java | @Override
public INDArray average(INDArray target, INDArray[] arrays) {
if (arrays == null || arrays.length == 0)
throw new RuntimeException("Input arrays are missing");
if (arrays.length == 1) {
//Edge case - average 1 array - no op
if(target == null){
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128,750 | deeplearning4j/deeplearning4j | nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/NDArrayMessage.java | NDArrayMessage.numChunksForMessage | public static int numChunksForMessage(NDArrayMessage message, int chunkSize) {
int sizeOfMessage = NDArrayMessage.byteBufferSizeForMessage(message);
int numMessages = sizeOfMessage / chunkSize;
//increase by 1 for padding
if (numMessages * chunkSize < sizeOfMessage)
numMessag... | java | public static int numChunksForMessage(NDArrayMessage message, int chunkSize) {
int sizeOfMessage = NDArrayMessage.byteBufferSizeForMessage(message);
int numMessages = sizeOfMessage / chunkSize;
//increase by 1 for padding
if (numMessages * chunkSize < sizeOfMessage)
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128,751 | deeplearning4j/deeplearning4j | nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/NDArrayMessage.java | NDArrayMessage.chunkedMessages | public static NDArrayMessage[] chunkedMessages(NDArrayMessage arrayMessage, int chunkSize) {
int sizeOfMessage = NDArrayMessage.byteBufferSizeForMessage(arrayMessage) - 4;
int numMessages = sizeOfMessage / chunkSize;
ByteBuffer direct = NDArrayMessage.toBuffer(arrayMessage).byteBuffer();
... | java | public static NDArrayMessage[] chunkedMessages(NDArrayMessage arrayMessage, int chunkSize) {
int sizeOfMessage = NDArrayMessage.byteBufferSizeForMessage(arrayMessage) - 4;
int numMessages = sizeOfMessage / chunkSize;
ByteBuffer direct = NDArrayMessage.toBuffer(arrayMessage).byteBuffer();
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128,752 | deeplearning4j/deeplearning4j | nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/NDArrayMessage.java | NDArrayMessage.getCurrentTimeUtc | public static long getCurrentTimeUtc() {
Instant instant = Instant.now();
ZonedDateTime dateTime = instant.atZone(ZoneOffset.UTC);
return dateTime.toInstant().toEpochMilli();
} | java | public static long getCurrentTimeUtc() {
Instant instant = Instant.now();
ZonedDateTime dateTime = instant.atZone(ZoneOffset.UTC);
return dateTime.toInstant().toEpochMilli();
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128,753 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/rng/deallocator/NativeRandomDeallocator.java | NativeRandomDeallocator.trackStatePointer | public void trackStatePointer(NativePack random) {
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referenceMap.put(random.getStatePointer().address(), reference);
}
} | java | public void trackStatePointer(NativePack random) {
if (random.getStatePointer() != null) {
GarbageStateReference reference = new GarbageStateReference(random, queue);
referenceMap.put(random.getStatePointer().address(), reference);
}
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128,754 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.relocate | @Override
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//log.info("RELOCATE CALLED: [" +currentStatus+ "] -> ["+targetStatus+"]");
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//log.info("RELOCATE CALLED: [" +currentStatus+ "] -> ["+targetStatus+"]");
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128,755 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.copyforward | @Override
@Deprecated
public void copyforward(AllocationPoint point, AllocationShape shape) {
/*
Technically that's just a case for relocate, with source as HOST and target point.getAllocationStatus()
*/
log.info("copyforward() called on tp[" + point.getObjectId() + "], shap... | java | @Override
@Deprecated
public void copyforward(AllocationPoint point, AllocationShape shape) {
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Technically that's just a case for relocate, with source as HOST and target point.getAllocationStatus()
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log.info("copyforward() called on tp[" + point.getObjectId() + "], shap... | [
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128,756 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.fallback | @Override
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throw new IllegalStateException("Can't fallback from [" + point.getAllocationStatus() + "]");
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@Deprecated
public void fallback(AllocationPoint point, AllocationShape shape) {
throw new IllegalStateException("Can't fallback from [" + point.getAllocationStatus() + "]");
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128,757 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.free | @Override
public void free(AllocationPoint point, AllocationStatus target) {
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//zeroAllocations.get(point.getBucketId()).remove(point.getObjectId());
... | java | @Override
public void free(AllocationPoint point, AllocationStatus target) {
//if (point.getAllocationStatus() == AllocationStatus.DEVICE)
//deviceAllocations.get(point.getDeviceId()).remove(point.getObjectId());
//zeroAllocations.get(point.getBucketId()).remove(point.getObjectId());
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128,758 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.memcpyAsync | @Override
public void memcpyAsync(DataBuffer dstBuffer, Pointer srcPointer, long length, long dstOffset) {
AllocationPoint point = ((BaseCudaDataBuffer) dstBuffer).getAllocationPoint();
// we update host memory regardless.
//Pointer dP = new Pointer((point.getAllocationStatus() == Allocation... | java | @Override
public void memcpyAsync(DataBuffer dstBuffer, Pointer srcPointer, long length, long dstOffset) {
AllocationPoint point = ((BaseCudaDataBuffer) dstBuffer).getAllocationPoint();
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128,759 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.memcpySpecial | @Override
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public void memcpySpecial(DataBuffer dstBuffer, Pointer srcPointer, long length, long dstOffset) {
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128,760 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.promoteObject | @Override
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if (configuration.getMemoryModel() == Configuration.Memory... | java | @Override
public boolean promoteObject(DataBuffer buffer) {
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128,761 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.getAllocationStatistics | @Override
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for (Integer deviceId : configuration.getAvailableDevices()) {
ta... | java | @Override
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128,762 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.getAllocatedHostObjects | @Override
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128,763 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.getAllocatedHostObjects | @Override
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}
return counter.get();
} | java | @Override
public long getAllocatedHostObjects() {
AtomicLong counter = new AtomicLong(0);
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128,764 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.getDeviceTrackingPoints | @Override
public Set<Long> getDeviceTrackingPoints(Integer deviceId) {
return deviceAllocations.get(deviceId).keySet();
} | java | @Override
public Set<Long> getDeviceTrackingPoints(Integer deviceId) {
return deviceAllocations.get(deviceId).keySet();
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128,765 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.getHostTrackingPoints | @Override
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}
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} | java | @Override
public Set<Long> getHostTrackingPoints(Long bucketId) {
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return new HashSet<>();
}
return zeroAllocations.get(bucketId).keySet();
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128,766 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.purgeDeviceObject | @Override
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boolean copyback) {
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return;
flowController.waitTillReleased(point);
free(point, Allocat... | java | @Override
public void purgeDeviceObject(Long threadId, Integer deviceId, Long objectId, AllocationPoint point,
boolean copyback) {
if (point.getAllocationStatus() != AllocationStatus.DEVICE)
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flowController.waitTillReleased(point);
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128,767 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.purgeZeroObject | @Override
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// we call for caseless deallocation here
//JCudaDriver.cuCtxSetCurrent(contextPool.getCuCont... | java | @Override
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128,768 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.initCudaContextForThread | protected void initCudaContextForThread(Long threadId) {
// we set device to be used prior to stream creation
nativeOps.setDevice(getDeviceId());
CudaContext context = new CudaContext();
context.initHandle();
context.initOldStream();
context.initStream();
conte... | java | protected void initCudaContextForThread(Long threadId) {
// we set device to be used prior to stream creation
nativeOps.setDevice(getDeviceId());
CudaContext context = new CudaContext();
context.initHandle();
context.initOldStream();
context.initStream();
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128,769 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/handler/impl/CudaZeroHandler.java | CudaZeroHandler.synchronizeThreadDevice | @Override
public void synchronizeThreadDevice(Long threadId, Integer deviceId, AllocationPoint point) {
// we synchronize only if this AllocationPoint was used within device context, so for multiple consequent syncs only first one will be issued
flowController.synchronizeToHost(point);
} | java | @Override
public void synchronizeThreadDevice(Long threadId, Integer deviceId, AllocationPoint point) {
// we synchronize only if this AllocationPoint was used within device context, so for multiple consequent syncs only first one will be issued
flowController.synchronizeToHost(point);
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128,770 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/util/ReflectionUtils.java | ReflectionUtils.setJobConf | private static void setJobConf(Object theObject, Configuration conf) {
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try {
Class<?> jobConfClass = conf.g... | java | private static void setJobConf(Object theObject, Configuration conf) {
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128,771 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/util/ReflectionUtils.java | ReflectionUtils.copy | @SuppressWarnings("unchecked")
public static <T> T copy(Configuration conf, T src, T dst) throws IOException {
CopyInCopyOutBuffer buffer = cloneBuffers.get();
buffer.outBuffer.reset();
SerializationFactory factory = getFactory(conf);
Class<T> cls = (Class<T>) src.getClass();
... | java | @SuppressWarnings("unchecked")
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128,772 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/function/FunctionalUtils.java | FunctionalUtils.cogroup | public static <K,V> Map<K,Pair<List<V>,List<V>>> cogroup(List<Pair<K,V>> left,List<Pair<K,V>> right) {
Map<K,Pair<List<V>,List<V>>> ret = new HashMap<>();
//group by key first to consolidate values
Map<K,List<V>> leftMap = groupByKey(left);
Map<K,List<V>> rightMap = groupByKey(right);
... | java | public static <K,V> Map<K,Pair<List<V>,List<V>>> cogroup(List<Pair<K,V>> left,List<Pair<K,V>> right) {
Map<K,Pair<List<V>,List<V>>> ret = new HashMap<>();
//group by key first to consolidate values
Map<K,List<V>> leftMap = groupByKey(left);
Map<K,List<V>> rightMap = groupByKey(right);
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128,773 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/function/FunctionalUtils.java | FunctionalUtils.groupByKey | public static <K,V> Map<K,List<V>> groupByKey(List<Pair<K,V>> listInput) {
Map<K,List<V>> ret = new HashMap<>();
for(Pair<K,V> pair : listInput) {
List<V> currList = ret.get(pair.getFirst());
if(currList == null) {
currList = new ArrayList<>();
ret... | java | public static <K,V> Map<K,List<V>> groupByKey(List<Pair<K,V>> listInput) {
Map<K,List<V>> ret = new HashMap<>();
for(Pair<K,V> pair : listInput) {
List<V> currList = ret.get(pair.getFirst());
if(currList == null) {
currList = new ArrayList<>();
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128,774 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/DifferentialFunctionClassHolder.java | DifferentialFunctionClassHolder.getOpDefByTensorflowName | public OpDef getOpDefByTensorflowName(String name) {
if(!tensorflowOpDescriptors.containsKey(name)) {
throw new ND4JIllegalStateException("No op found with name " + name);
}
return tensorflowOpDescriptors.get(name);
} | java | public OpDef getOpDefByTensorflowName(String name) {
if(!tensorflowOpDescriptors.containsKey(name)) {
throw new ND4JIllegalStateException("No op found with name " + name);
}
return tensorflowOpDescriptors.get(name);
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128,775 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/storage/CompressedRamStorage.java | CompressedRamStorage.storeIfAbsent | @Override
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lock.writeLock().lock();
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return false;
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retur... | java | @Override
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try {
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128,776 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/storage/CompressedRamStorage.java | CompressedRamStorage.get | @Override
public INDArray get(T key) {
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// TODO: we don't save decompressed entries here, but something like LRU m... | java | @Override
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128,777 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/storage/CompressedRamStorage.java | CompressedRamStorage.containsKey | @Override
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if (emulateIsAbsent)
lock.readLock().unlock();
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} | java | @Override
public boolean containsKey(T key) {
try {
if (emulateIsAbsent)
lock.readLock().lock();
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} finally {
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128,778 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/storage/CompressedRamStorage.java | CompressedRamStorage.clear | @Override
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public void clear() {
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compressedEntries.clear();
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128,779 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/storage/CompressedRamStorage.java | CompressedRamStorage.drop | @Override
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128,780 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/storage/CompressedRamStorage.java | CompressedRamStorage.size | @Override
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128,781 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/word2vec/wordstore/inmemory/InMemoryLookupCache.java | InMemoryLookupCache.incrementWordCount | @Override
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VocabWord... | java | @Override
public synchronized void incrementWordCount(String word, int increment) {
if (word == null || word.isEmpty())
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128,782 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/rng/distribution/impl/SaddlePointExpansion.java | SaddlePointExpansion.logBinomialProbability | public static double logBinomialProbability(int x, int n, double p, double q) {
double ret;
if (x == 0) {
if (p < 0.1) {
ret = -getDeviancePart(n, n * q) - n * p;
} else {
ret = n * FastMath.log(q);
}
} else if (x == n) {
... | java | public static double logBinomialProbability(int x, int n, double p, double q) {
double ret;
if (x == 0) {
if (p < 0.1) {
ret = -getDeviancePart(n, n * q) - n * p;
} else {
ret = n * FastMath.log(q);
}
} else if (x == n) {
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128,783 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/PatriciaTrieFormatter.java | PatriciaTrieFormatter.formatPointer | private String formatPointer(PatriciaTrie.PatriciaNode<V> from, PatriciaTrie.PatriciaNode<V> to, String label,
String tailport) {
StringBuilder builder = new StringBuilder();
builder.append(getNodeId(from));
builder.append(" -> ");
builder.append(getNodeId(to));
... | java | private String formatPointer(PatriciaTrie.PatriciaNode<V> from, PatriciaTrie.PatriciaNode<V> to, String label,
String tailport) {
StringBuilder builder = new StringBuilder();
builder.append(getNodeId(from));
builder.append(" -> ");
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128,784 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/PatriciaTrieFormatter.java | PatriciaTrieFormatter.formatNodeLabel | private String formatNodeLabel(PatriciaTrie.PatriciaNode<V> node, KeyMapper<String> keyMapper,
boolean formatBitString) {
StringBuilder builder = new StringBuilder();
builder.append("<<table border=\"0\" cellborder=\"0\">");
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builder.append("<tr><td>");
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@param node node to format
@param keyMapper keymapper to map keys to bits
@param formatBitString true if the bits for this key should be included in the node
@return formatted node, not null | [
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] | effce52f2afd7eeb53c5bcca699fcd90bd06822f | https://github.com/deeplearning4j/deeplearning4j/blob/effce52f2afd7eeb53c5bcca699fcd90bd06822f/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/PatriciaTrieFormatter.java#L195-L233 |
128,785 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/PatriciaTrieFormatter.java | PatriciaTrieFormatter.getNodeId | private String getNodeId(PatriciaTrie.PatriciaNode<V> node) {
if (node == null) {
return "null";
} else {
return node.getKey();
}
} | java | private String getNodeId(PatriciaTrie.PatriciaNode<V> node) {
if (node == null) {
return "null";
} else {
return node.getKey();
}
} | [
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128,786 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/main/java/org/deeplearning4j/spark/util/SparkUtils.java | SparkUtils.checkKryoConfiguration | public static boolean checkKryoConfiguration(JavaSparkContext javaSparkContext, Logger log) {
//Check if kryo configuration is correct:
String serializer = javaSparkContext.getConf().get("spark.serializer", null);
if (serializer != null && serializer.equals("org.apache.spark.serializer.KryoSeria... | java | public static boolean checkKryoConfiguration(JavaSparkContext javaSparkContext, Logger log) {
//Check if kryo configuration is correct:
String serializer = javaSparkContext.getConf().get("spark.serializer", null);
if (serializer != null && serializer.equals("org.apache.spark.serializer.KryoSeria... | [
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@param javaSparkContext Spark context
@param log Logger to log messages to
@return True if ok (no kryo, or correct kryo setup) | [
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128,787 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/main/java/org/deeplearning4j/spark/util/SparkUtils.java | SparkUtils.shuffleExamples | public static JavaRDD<DataSet> shuffleExamples(JavaRDD<DataSet> rdd, int newBatchSize, int numPartitions) {
//Step 1: split into individual examples, mapping to a pair RDD (random key in range 0 to numPartitions)
JavaPairRDD<Integer, DataSet> singleExampleDataSets =
rdd.flatMapT... | java | public static JavaRDD<DataSet> shuffleExamples(JavaRDD<DataSet> rdd, int newBatchSize, int numPartitions) {
//Step 1: split into individual examples, mapping to a pair RDD (random key in range 0 to numPartitions)
JavaPairRDD<Integer, DataSet> singleExampleDataSets =
rdd.flatMapT... | [
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128,788 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaGridExecutioner.java | CudaGridExecutioner.pushToGrid | protected void pushToGrid(OpDescriptor descriptor, boolean flush) {
// we should just add op to queue here
//deviceQueues.get().add(descriptor);
// FIXME: following code should be removed, since it's just executing supers instead of batching
execCounter.incrementAndGet();
Op ... | java | protected void pushToGrid(OpDescriptor descriptor, boolean flush) {
// we should just add op to queue here
//deviceQueues.get().add(descriptor);
// FIXME: following code should be removed, since it's just executing supers instead of batching
execCounter.incrementAndGet();
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128,789 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaGridExecutioner.java | CudaGridExecutioner.isMatchingZX | protected boolean isMatchingZX(Op opA, Op opB) {
if (opA.x() == opB.x() && opA.z() == opB.z() && opA.x() == opB.z())
return true;
return false;
} | java | protected boolean isMatchingZX(Op opA, Op opB) {
if (opA.x() == opB.x() && opA.z() == opB.z() && opA.x() == opB.z())
return true;
return false;
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128,790 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaGridExecutioner.java | CudaGridExecutioner.isMatchingZXY | protected boolean isMatchingZXY(Op opA, Op opB) {
if (opA.z() == opB.x() || opA.z() == opB.y())
return true;
return false;
} | java | protected boolean isMatchingZXY(Op opA, Op opB) {
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return true;
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128,791 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaGridExecutioner.java | CudaGridExecutioner.pointerizeOp | protected GridPointers pointerizeOp(Op op, int... dimensions) {
GridPointers pointers = new GridPointers(op, dimensions);
AtomicAllocator allocator = AtomicAllocator.getInstance();
// CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(op.z(), op.x(), o... | java | protected GridPointers pointerizeOp(Op op, int... dimensions) {
GridPointers pointers = new GridPointers(op, dimensions);
AtomicAllocator allocator = AtomicAllocator.getInstance();
// CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(op.z(), op.x(), o... | [
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128,792 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaGridExecutioner.java | CudaGridExecutioner.aggregate | @Override
public void aggregate(Aggregate op, long key) {
int deviceId = Nd4j.getAffinityManager().getDeviceForCurrentThread();
if (opCounter.get() == null)
opCounter.set(new AtomicLong(0));
// we enqueue op for specific device here
aggregates.get(deviceId).add(new Aggre... | java | @Override
public void aggregate(Aggregate op, long key) {
int deviceId = Nd4j.getAffinityManager().getDeviceForCurrentThread();
if (opCounter.get() == null)
opCounter.set(new AtomicLong(0));
// we enqueue op for specific device here
aggregates.get(deviceId).add(new Aggre... | [
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128,793 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/util/CudaArgs.java | CudaArgs.getModuleNameFor | public static String getModuleNameFor(Op op) {
//String functionName = op instanceof TransformOp || op instanceof ReduceOp || op instanceof IndexAccumulation ? op.opName() + "_strided" : op.opName();
String moduleName = null;
if (op instanceof ReduceOp) {
moduleName = "reduce";
... | java | public static String getModuleNameFor(Op op) {
//String functionName = op instanceof TransformOp || op instanceof ReduceOp || op instanceof IndexAccumulation ? op.opName() + "_strided" : op.opName();
String moduleName = null;
if (op instanceof ReduceOp) {
moduleName = "reduce";
... | [
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128,794 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/util/CudaArgs.java | CudaArgs.convertMPtoCores | public static int convertMPtoCores(int ccMajor, int ccMinor, int numberOfProcessors) {
// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
if (ccMajor == 1)
return 8;
if (ccMajor == 2 && ccMinor == 1)
return 48;
if (ccMa... | java | public static int convertMPtoCores(int ccMajor, int ccMinor, int numberOfProcessors) {
// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
if (ccMajor == 1)
return 8;
if (ccMajor == 2 && ccMinor == 1)
return 48;
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128,795 | deeplearning4j/deeplearning4j | rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/a3c/discrete/A3CThreadDiscrete.java | A3CThreadDiscrete.calcGradient | @Override
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MiniTrans<Integer> minTrans = rewards.pop();
int size = rewards.size();
//if recurrent then train as a time serie with a batch size of 1
boolean recurrent = getAsyncGlobal().getCurrent()... | java | @Override
public Gradient[] calcGradient(IActorCritic iac, Stack<MiniTrans<Integer>> rewards) {
MiniTrans<Integer> minTrans = rewards.pop();
int size = rewards.size();
//if recurrent then train as a time serie with a batch size of 1
boolean recurrent = getAsyncGlobal().getCurrent()... | [
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128,796 | deeplearning4j/deeplearning4j | nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/chunk/InMemoryChunkAccumulator.java | InMemoryChunkAccumulator.numChunksSoFar | @Override
public int numChunksSoFar(String id) {
if (!chunks.containsKey(id))
return 0;
return chunks.get(id).size();
} | java | @Override
public int numChunksSoFar(String id) {
if (!chunks.containsKey(id))
return 0;
return chunks.get(id).size();
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128,797 | deeplearning4j/deeplearning4j | nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/chunk/InMemoryChunkAccumulator.java | InMemoryChunkAccumulator.allPresent | @Override
public boolean allPresent(String id) {
if (!chunks.containsKey(id))
return false;
List<NDArrayMessageChunk> chunkList = chunks.get(id);
return chunkList.size() == chunkList.get(0).getNumChunks();
} | java | @Override
public boolean allPresent(String id) {
if (!chunks.containsKey(id))
return false;
List<NDArrayMessageChunk> chunkList = chunks.get(id);
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] | effce52f2afd7eeb53c5bcca699fcd90bd06822f | https://github.com/deeplearning4j/deeplearning4j/blob/effce52f2afd7eeb53c5bcca699fcd90bd06822f/nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/chunk/InMemoryChunkAccumulator.java#L58-L64 |
128,798 | deeplearning4j/deeplearning4j | nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/chunk/InMemoryChunkAccumulator.java | InMemoryChunkAccumulator.reassemble | @Override
public NDArrayMessage reassemble(String id) {
List<NDArrayMessageChunk> chunkList = chunks.get(id);
if (chunkList.size() != chunkList.get(0).getNumChunks())
throw new IllegalStateException("Unable to reassemble message chunk " + id + " missing "
+ (c... | java | @Override
public NDArrayMessage reassemble(String id) {
List<NDArrayMessageChunk> chunkList = chunks.get(id);
if (chunkList.size() != chunkList.get(0).getNumChunks())
throw new IllegalStateException("Unable to reassemble message chunk " + id + " missing "
+ (c... | [
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from a set of chunks
Note that once reassemble is called,
the associated chunk lists will automatically
be removed from storage.
@param id the id to reassemble
@return the reassembled message | [
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] | effce52f2afd7eeb53c5bcca699fcd90bd06822f | https://github.com/deeplearning4j/deeplearning4j/blob/effce52f2afd7eeb53c5bcca699fcd90bd06822f/nd4j/nd4j-serde/nd4j-aeron/src/main/java/org/nd4j/aeron/ipc/chunk/InMemoryChunkAccumulator.java#L78-L96 |
128,799 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/KerasLoss.java | KerasLoss.getLossLayer | public FeedForwardLayer getLossLayer(InputType type) throws UnsupportedKerasConfigurationException {
if (type instanceof InputType.InputTypeFeedForward) {
this.layer = new LossLayer.Builder(loss).name(this.layerName).build();
}
else if (type instanceof InputType.InputTypeRecurrent) ... | java | public FeedForwardLayer getLossLayer(InputType type) throws UnsupportedKerasConfigurationException {
if (type instanceof InputType.InputTypeFeedForward) {
this.layer = new LossLayer.Builder(loss).name(this.layerName).build();
}
else if (type instanceof InputType.InputTypeRecurrent) ... | [
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... | Get DL4J LossLayer.
@return LossLayer | [
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] | effce52f2afd7eeb53c5bcca699fcd90bd06822f | https://github.com/deeplearning4j/deeplearning4j/blob/effce52f2afd7eeb53c5bcca699fcd90bd06822f/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/KerasLoss.java#L97-L111 |
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