org.nd4j.linalg.api.ops.impl.loss.bp.SoftmaxCrossEntropyLossBp Maven / Gradle / Ivy
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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.
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* * information regarding copyright ownership.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.loss.bp;
import lombok.NoArgsConstructor;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import java.util.Arrays;
import java.util.List;
@NoArgsConstructor
public class SoftmaxCrossEntropyLossBp extends BaseLossBp {
private double labelSmoothing = 0.0;
public SoftmaxCrossEntropyLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights, SDVariable labels,
double labelSmoothing) {
super(sameDiff, lossReduce, logits, weights, labels);
this.labelSmoothing = labelSmoothing;
addArgs();
}
public void addArgs() {
super.addArgs();
addTArgument(labelSmoothing);
}
@Override
public String opName() {
return "softmax_cross_entropy_loss_grad";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && (inputDataTypes.size() == 2 || inputDataTypes.size() == 3),
"Expected 2 or 3 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Arrays.asList(inputDataTypes.get(0), inputDataTypes.get(1), inputDataTypes.get(2)); //Same as predictions
}
}