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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
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.transforms;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseTransformOp;
import java.util.Arrays;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
/**
* Leaky Rectified linear unit. Default alpha=0.01, cutoff=0
* Out(x) = alpha*x if x<0
* Out(x) = x if x >= 0
* Leaky ReLU may avoid zero gradient "dying ReLU" problem by having non-zero
* gradient below 0.
* See for example http://arxiv.org/abs/1505.00853 for a comparison of
* ReLU variants.
*
* @author Alex Black
*/
public class LeakyReLU extends BaseTransformOp {
public static final double DEFAULT_ALPHA = 0.01;
private double alpha = DEFAULT_ALPHA;
public LeakyReLU(SameDiff sameDiff, SDVariable i_v, boolean inPlace, double alpha) {
super(sameDiff, i_v, inPlace);
this.alpha = alpha;
this.extraArgs = new Object[]{alpha};
}
public LeakyReLU(SameDiff sameDiff, SDVariable i_v, int[] shape, boolean inPlace, Object[] extraArgs, double alpha) {
super(sameDiff, i_v, shape, inPlace, extraArgs);
this.alpha = alpha;
this.extraArgs = new Object[]{alpha};
}
public LeakyReLU(SameDiff sameDiff, SDVariable i_v, Object[] extraArgs, double alpha) {
super(sameDiff, i_v, extraArgs);
this.alpha = alpha;
this.extraArgs = new Object[]{alpha};
}
public LeakyReLU() {
super();
}
public LeakyReLU(INDArray x, double alpha) {
super(x);
this.alpha = alpha;
init(x, y, z, n); //Need to re-init to properly set alpha in extra args array
}
public LeakyReLU(INDArray x, INDArray z, double alpha) {
super(x, z);
this.alpha = alpha;
init(x, y, z, n);
}
public LeakyReLU(INDArray x, INDArray z, long n, double alpha) {
super(x, z, n);
this.alpha = alpha;
init(x, y, z, n);
}
public LeakyReLU(INDArray x, INDArray y, INDArray z, long n, double alpha) {
super(x, y, z, n);
this.alpha = alpha;
init(x, y, z, n);
}
public LeakyReLU(INDArray x, INDArray z) {
super(x, z);
}
public LeakyReLU(INDArray x, INDArray z, long n) {
super(x, z, n);
}
public LeakyReLU(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
}
public LeakyReLU(INDArray x) {
super(x);
this.extraArgs = new Object[]{alpha};
}
@Override
public int opNum() {
return 31;
}
@Override
public String opName() {
return "leakyrelu";
}
@Override
public void init(INDArray x, INDArray y, INDArray z, long n) {
super.init(x, y, z, n);
this.extraArgs = new Object[]{alpha};
}
@Override
public String onnxName() {
return "LeakyRelu";
}
@Override
public String tensorflowName() {
return "LeakyRelu";
}
@Override
public List doDiff(List i_v) {
SDVariable ret = f().leakyReluDerivative(arg(), alpha).mul(i_v.get(0));
return Arrays.asList(ret);
}
}