org.nd4j.linalg.activations.impl.ActivationRReLU Maven / Gradle / Ivy
/* *****************************************************************************
* 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
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.activations.impl;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.linalg.activations.BaseActivationFunction;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.scalar.RectifiedLinear;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.conditions.Conditions;
import org.nd4j.shade.jackson.annotation.JsonIgnoreProperties;
/**
* f(x) = max(0,x) + alpha * min(0, x)
*
* alpha is drawn from uniform(l,u) during training and is set to l+u/2 during test
* l and u default to 1/8 and 1/3 respectively
*
*
* Empirical Evaluation of Rectified Activations in Convolutional Network
*/
@EqualsAndHashCode(callSuper = false)
@JsonIgnoreProperties({"alpha"})
@Getter
public class ActivationRReLU extends BaseActivationFunction {
public static final double DEFAULT_L = 1.0 / 8;
public static final double DEFAULT_U = 1.0 / 3;
private double l, u;
private transient INDArray alpha; //don't need to write to json, when streaming
public ActivationRReLU() {
this(DEFAULT_L, DEFAULT_U);
}
public ActivationRReLU(double l, double u) {
if (l > u) {
throw new IllegalArgumentException("Cannot have lower value (" + l + ") greater than upper (" + u + ")");
}
this.l = l;
this.u = u;
}
@Override
public INDArray getActivation(INDArray in, boolean training) {
if (training) {
try(MemoryWorkspace ignored = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
this.alpha = Nd4j.rand(l, u, Nd4j.getRandom(), in.shape());
}
INDArray inTimesAlpha = in.mul(alpha);
BooleanIndexing.replaceWhere(in, inTimesAlpha, Conditions.lessThan(0));
} else {
this.alpha = null;
double a = 0.5 * (l + u);
return Nd4j.getExecutioner().exec(new RectifiedLinear(in, a));
}
return in;
}
@Override
public Pair backprop(INDArray in, INDArray epsilon) {
assertShape(in, epsilon);
INDArray dLdz = Nd4j.ones(in.shape());
BooleanIndexing.replaceWhere(dLdz, alpha, Conditions.lessThanOrEqual(0.0));
dLdz.muli(epsilon);
return new Pair<>(dLdz, null);
}
@Override
public String toString() {
return "rrelu(l=" + l + ", u=" + u + ")";
}
}