org.nd4j.linalg.api.ops.random.custom.RandomBernoulli Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.random.custom;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
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;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Random bernoulli distribution: p(x=1) = p, p(x=0) = 1-p
* i.e., output is 0 or 1 with probability p.
*
* @author Alex Black
*/
@Slf4j
public class RandomBernoulli extends DynamicCustomOp {
private double p = 0.0;
public RandomBernoulli() {
//
}
public RandomBernoulli(SameDiff sd, SDVariable shape, double p){
super(null, sd, new SDVariable[]{shape});
Preconditions.checkState(p >= 0 && p <= 1.0, "Probability must be between 0 and 1 - got %s", p);
this.p = p;
addTArgument(p);
}
public RandomBernoulli(INDArray shape, INDArray out, double p){
super(null, new INDArray[]{shape}, new INDArray[]{out}, Collections.singletonList(p), (List)null);
Preconditions.checkState(p >= 0 && p <= 1.0, "Probability must be between 0 and 1 - got %s", p);
}
@Override
public String opName() {
return "random_bernoulli";
}
@Override
public List doDiff(List gradients){
return Collections.singletonList(sameDiff.zerosLike(arg()));
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes);
//Input data type specifies the shape; output data type should be any float
//TODO MAKE CONFIGUREABLE - https://github.com/deeplearning4j/deeplearning4j/issues/6854
return Collections.singletonList(DataType.FLOAT);
}
}