org.nd4j.linalg.api.ops.impl.transforms.gradient.Relu6Derivative Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.transforms.gradient;
import lombok.NonNull;
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 java.util.Collections;
import java.util.List;
import org.nd4j.linalg.api.ops.impl.transforms.same.Identity;
/**
* Derivative of Rectified linear unit 6, i.e. min(max(input, cutoff), 6), where cutoff can be chosen.
*
* @author Alex Black
*/
public class Relu6Derivative extends DynamicCustomOp {
private static final double DEFAULT_CUTOFF = 0.0;
private double cutoff = DEFAULT_CUTOFF;
public Relu6Derivative(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, double cutoff) {
super("relu6_bp", sameDiff, new SDVariable[]{i_v1, i_v2});
this.cutoff = cutoff;
this.extraArgs = new Object[]{cutoff};
}
public Relu6Derivative() {
this.extraArgs = new Object[]{cutoff};
}
public Relu6Derivative(@NonNull INDArray input, @NonNull INDArray gradient, INDArray output){
this(input, gradient, output, DEFAULT_CUTOFF);
}
public Relu6Derivative(@NonNull INDArray input, @NonNull INDArray gradient, INDArray output, double cutoff){
super(new INDArray[]{input, gradient}, wrapOrNull(output));
this.cutoff = cutoff;
this.extraArgs = new Object[]{cutoff};
}
@Override
public int opNum() {
return 0;
}
@Override
public String opName() {
return "relu6_bp";
}
@Override
public String onnxName() { throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public List doDiff(List i_v) {
throw new UnsupportedOperationException("Not supported");
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}