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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.lossfunctions.impl;
import lombok.EqualsAndHashCode;
import onnx.OnnxProto3;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.Op;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.List;
import java.util.Map;
/**
* Mean absolute error loss function: L = 1/N sum_i abs(predicted_i - actual_i)
* See also {@link LossL1} for a mathematically similar loss function (LossL1 does not have division by N, where N is output size)
*
* @author Susan Eraly
*/
@EqualsAndHashCode(callSuper = true)
public class LossMAE extends LossL1 {
public LossMAE() {
}
/**
* Mean Absolute Error loss function where each the output is (optionally) weighted/scaled by a flags scalar value.
* Note that the weights array must be a row vector, of length equal to the labels/output dimension 1 size.
* A weight vector of 1s should give identical results to no weight vector.
*
* @param weights Weights array (row vector). May be null.
*/
public LossMAE(INDArray weights) {
super(weights);
}
@Override
public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask,
boolean average) {
double score = super.computeScore(labels, preOutput, activationFn, mask, average);
score /= (labels.size(1));
return score;
}
@Override
public INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
INDArray scoreArr = super.computeScoreArray(labels, preOutput, activationFn, mask);
scoreArr.divi(scoreArr.size(1));
return scoreArr;
}
@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
INDArray gradients = super.computeGradient(labels, preOutput, activationFn, mask);
gradients.divi(labels.size(1));
return gradients;
}
/**
* The opName of this function
*
* @return
*/
@Override
public String name() {
return toString();
}
@Override
public String toString() {
if (weights == null)
return "LossMAE()";
return "LossMAE(weights=" + weights + ")";
}
@Override
public List doDiff(List f1) {
return null;
}
@Override
public String opName() {
return name();
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op name found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op name found for " + opName());
}
}
