org.nd4j.linalg.api.ops.impl.nlp.CbowRound Maven / Gradle / Ivy
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* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * under the License.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.nlp;
import lombok.NonNull;
import lombok.val;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;
public class CbowRound extends DynamicCustomOp {
public CbowRound(){ }
/**
* hs round
*
* @param target
* @param context
* @param syn0
* @param syn1
* @param expTable
* @param alpha
* @param nextRandom
* @param inferenceVector
*/
public CbowRound(int target, @NonNull int[] context, @NonNull int[] lockedWords, @NonNull INDArray syn0, @NonNull INDArray syn1, @NonNull INDArray expTable, @NonNull int[] indices, @NonNull byte[] codes, double alpha, long nextRandom, @NonNull INDArray inferenceVector, int numLabels) {
this(Nd4j.scalar(target), Nd4j.createFromArray(context), Nd4j.createFromArray(lockedWords), Nd4j.empty(DataType.INT), syn0, syn1, Nd4j.empty(syn1.dataType()), expTable, Nd4j.empty(syn1.dataType()), Nd4j.createFromArray(indices), Nd4j.createFromArray(codes), 0, Nd4j.scalar(alpha), Nd4j.scalar(nextRandom), inferenceVector, Nd4j.scalar(numLabels), inferenceVector.isEmpty(), 1);
}
/**
* ns round
*
* @param target
* @param context
* @param ngStarter
* @param syn0
* @param syn1Neg
* @param expTable
* @param negTable
* @param alpha
* @param nextRandom
* @param inferenceVector
*/
public CbowRound(int target, @NonNull int[] context, @NonNull int[] lockedWords, int ngStarter, @NonNull INDArray syn0, @NonNull INDArray syn1Neg, @NonNull INDArray expTable, @NonNull INDArray negTable, int nsRounds, double alpha, long nextRandom, @NonNull INDArray inferenceVector, int numLabels) {
this(Nd4j.scalar(target), Nd4j.createFromArray(context), Nd4j.createFromArray(lockedWords), Nd4j.scalar(ngStarter), syn0, Nd4j.empty(syn0.dataType()), syn1Neg, expTable, negTable, Nd4j.empty(DataType.INT), Nd4j.empty(DataType.BYTE), nsRounds, Nd4j.scalar(alpha), Nd4j.scalar(nextRandom), inferenceVector, Nd4j.scalar(numLabels), inferenceVector.isEmpty(), 1);
}
/**
* full constructor
*
* @param target
* @param context
* @param ngStarter
* @param syn0
* @param syn1
* @param syn1Neg
* @param expTable
* @param negTable
* @param alpha
* @param nextRandom
* @param inferenceVector
*/
public CbowRound(@NonNull INDArray target, @NonNull INDArray context, @NonNull INDArray lockedWords, @NonNull INDArray ngStarter, @NonNull INDArray syn0, @NonNull INDArray syn1, @NonNull INDArray syn1Neg, @NonNull INDArray expTable, @NonNull INDArray negTable, @NonNull INDArray indices, @NonNull INDArray codes, int nsRounds, @NonNull INDArray alpha, @NonNull INDArray nextRandom, @NonNull INDArray inferenceVector, @NonNull INDArray numLabels, boolean trainWords, int numWorkers) {
inputArguments.add(target);
inputArguments.add(ngStarter);
inputArguments.add(context);
inputArguments.add(indices);
inputArguments.add(codes);
inputArguments.add(syn0);
inputArguments.add(syn1);
inputArguments.add(syn1Neg);
inputArguments.add(expTable);
inputArguments.add(negTable);
inputArguments.add(alpha);
inputArguments.add(nextRandom);
inputArguments.add(numLabels);
inputArguments.add(lockedWords);
inputArguments.add(inferenceVector);
// couple of options
iArguments.add((long) numWorkers);
iArguments.add((long) nsRounds);
bArguments.add(trainWords);
bArguments.add(!inferenceVector.isEmpty());
// this op is always inplace
setInPlace(true);
setInplaceCall(true);
for (val in:inputArguments)
outputArguments.add(in);
}
@Override
public String opName() {
return "cbow";
}
}