org.nd4j.linalg.api.ops.impl.loss.L2Loss 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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* * See the NOTICE file distributed with this work for additional
* * 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;
import lombok.NoArgsConstructor;
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 org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
/**
* L2 loss op wrapper
*/
@NoArgsConstructor
public class L2Loss extends DynamicCustomOp {
public L2Loss(SameDiff sameDiff, SDVariable var) {
super(sameDiff, new SDVariable[]{var});
}
public L2Loss(INDArray var){
super(new INDArray[]{var}, null);
}
@Override
public String opName() {
return "l2_loss";
}
@Override
public String tensorflowName() {
return "L2Loss";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected 1 input type for %s, got %s", getClass(), inputDataTypes);
Preconditions.checkState(inputDataTypes.get(0).isFPType(), "Input datatype must be floating point for %s, got %s", getClass(), inputDataTypes);
return inputDataTypes;
}
@Override
public List doDiff(List grad){
//L2 loss: L = 1/2 * sum(x_i^2)
//dL/dxi = xi
return Collections.singletonList(sameDiff.identity(arg()));
}
}