org.nd4j.linalg.activations.impl.ActivationReLU Maven / Gradle / Ivy
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* ******************************************************************************
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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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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
* * License for the specific language governing permissions and limitations
* * under the License.
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* * SPDX-License-Identifier: Apache-2.0
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*/
package org.nd4j.linalg.activations.impl;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import org.nd4j.linalg.api.ops.impl.scalar.*;
import org.nd4j.linalg.api.ops.impl.transforms.gradient.LeakyReLUBp;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.activations.BaseActivationFunction;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
/**
* f(x) = max(0, x)
*/
@EqualsAndHashCode(callSuper = false)
@Getter
public class ActivationReLU extends BaseActivationFunction {
private Double max;
private Double threshold;
private Double negativeSlope;
public ActivationReLU(){
this(null, null, null);
}
public ActivationReLU(Double maxValue, Double threshold, Double negativeSlope){
this.max = maxValue;
this.threshold = threshold;
this.negativeSlope = negativeSlope;
}
@Override
public INDArray getActivation(INDArray in, boolean training) {
if(negativeSlope != null || threshold != null){
double t = threshold == null ? 0.0 : threshold;
double ns = negativeSlope == null ? 0.0 : negativeSlope;
if(t == 0.0) {
Nd4j.getExecutioner().execAndReturn(new LeakyReLU(in, ns));
} else {
//Non-zero threshold, and non-zero slope
//TODO optimize this... but, extremely rare case in practice?
INDArray oneGte = in.gte(t).castTo(in.dataType());
INDArray oneLt = in.lt(t).castTo(in.dataType());
INDArray lower = oneLt.muli(ns).muli(in.sub(threshold));
INDArray upper = oneGte.muli(in);
in.assign(lower.addi(upper));
}
} else {
Nd4j.getExecutioner().exec(new RectifiedLinear(in, in));
}
if(max != null){
Nd4j.exec(new ScalarMin(in, null, in, max));
}
return in;
}
@Override
public Pair backprop(INDArray in, INDArray epsilon) {
assertShape(in, epsilon);
INDArray dLdz;
INDArray maxMask = (max == null || max == 0.0 ? null : in.lt(max));
if(negativeSlope != null || threshold != null){
double t = threshold == null ? 0.0 : threshold;
double ns = negativeSlope == null ? 0.0 : negativeSlope;
if(t == 0.0) {
dLdz = Nd4j.getExecutioner().exec(new LeakyReLUBp(in, epsilon, in.ulike(), ns))[0];
} else {
//Non-zero threshold, and non-zero slope
//TODO optimize this... but, extremely rare case in practice?
INDArray oneGte = in.gte(t).castTo(in.dataType());
INDArray oneLt = in.lt(t).castTo(in.dataType());
INDArray lower = oneLt.muli(ns);
INDArray upper = oneGte;
dLdz = in.assign(lower.addi(upper)).muli(epsilon);
}
} else {
dLdz = Nd4j.getExecutioner().exec(new RectifiedLinearDerivative(in, epsilon, in.ulike(), threshold == null ? 0.0 : threshold))[0];
}
if(maxMask != null){
dLdz.muli(maxMask);
}
return new Pair<>(dLdz, null);
}
@Override
public String toString() {
return "relu";
}
}