org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss Maven / Gradle / Ivy
The newest version!
/*
* ******************************************************************************
* *
* *
* * 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 lombok.NoArgsConstructor;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.loss.bp.SoftmaxCrossEntropyLossBp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.List;
import java.util.Map;
@NoArgsConstructor
public class SoftmaxCrossEntropyLoss extends BaseLoss {
public static final double DEFAULT_LABEL_SMOOTHING = 0.0;
private double labelSmoothing = 0.0;
public SoftmaxCrossEntropyLoss(SameDiff sameDiff, SDVariable labels, SDVariable logits,
SDVariable weights, LossReduce lossReduce, double labelSmoothing) {
this(sameDiff, lossReduce, logits, weights, labels, labelSmoothing);
}
public SoftmaxCrossEntropyLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights,
SDVariable labels,
double labelSmoothing) {
super(sameDiff, lossReduce, logits, weights, labels);
this.labelSmoothing = labelSmoothing;
tArguments.add(labelSmoothing);
}
public SoftmaxCrossEntropyLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights,
SDVariable labels) {
this(sameDiff, lossReduce, logits, weights, labels, 0.0);
}
public SoftmaxCrossEntropyLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce,
double labelSmoothing) {
super(lossReduce, predictions, weights, labels);
this.labelSmoothing = labelSmoothing;
tArguments.add(labelSmoothing);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode,
GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
super.addArgs();
tArguments.add(labelSmoothing);
}
@Override
public String opName() {
return "softmax_cross_entropy_loss";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "SoftmaxCrossEntropy";
}
@Override
public List doDiff(List grad) {
// No external gradient
// Args are: predictions, weights, label
if (tArguments.size() > 0) {
this.labelSmoothing = tArguments.get(tArguments.size() - 1);
}
return new SoftmaxCrossEntropyLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2), this.labelSmoothing)
.outputs();
}
}