All Downloads are FREE. Search and download functionalities are using the official Maven repository.
Please wait. This can take some minutes ...
Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
org.nd4j.linalg.api.ops.impl.loss.LogPoissonLoss Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.impl.loss;
import org.nd4j.autodiff.loss.LossReduce;
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.impl.loss.bp.LogPoissonLossBp;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
public class LogPoissonLoss extends BaseLoss {
private boolean full;
public LogPoissonLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels){
this(sameDiff, lossReduce, predictions, weights, labels, false);
}
public LogPoissonLoss(SameDiff sameDiff, SDVariable labels, SDVariable predictions, SDVariable weights,
LossReduce lossReduce, boolean full) {
this(sameDiff, lossReduce, predictions, weights, labels, full);
}
public LogPoissonLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, boolean full){
super(sameDiff, lossReduce, predictions, weights, labels);
this.full = full;
addArgs();
}
public LogPoissonLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce, boolean full){
super(lossReduce, predictions, weights, labels);
this.full = full;
addArgs();
}
public LogPoissonLoss(){ }
protected void addArgs(){
super.addArgs();
if(full){
iArguments.add((long) 1);
}
}
@Override
public void configureFromArguments() {
if(!iArguments.isEmpty()) {
this.lossReduce = LossReduce.values()[iArguments.get(0).intValue()];
}
}
@Override
public Map propertiesForFunction() {
Map ret = new HashMap<>();
ret.put("full",full);
ret.put("reductionMode",lossReduce.ordinal());
return ret;
}
@Override
public void setPropertiesForFunction(Map properties) {
if(properties.containsKey("reductionMode")) {
Integer reductionMode = getIntValueFromProperty("reductionMode",properties);
this.lossReduce = LossReduce.values()[reductionMode];
}
if(properties.containsKey("full")) {
Boolean full = getBooleanFromProperty("full",properties);
this.full = full;
}
}
@Override
public String opName() {
return "log_poisson_loss";
}
@Override
public List doDiff(List grad){
//No external gradient
//Args are: predictions, weights, label
return new LogPoissonLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2), full).outputs();
}
}