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/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* 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 lombok.Getter;
import lombok.Setter;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.ActivationSoftmax;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.shape.OneHot;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.conditions.Conditions;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.serde.jackson.shaded.NDArrayTextDeSerializer;
import org.nd4j.serde.jackson.shaded.NDArrayTextSerializer;
import org.nd4j.shade.jackson.annotation.JsonInclude;
import org.nd4j.shade.jackson.annotation.JsonProperty;
import org.nd4j.shade.jackson.databind.annotation.JsonDeserialize;
import org.nd4j.shade.jackson.databind.annotation.JsonSerialize;
/**
*
* Sparse Multi-Class Cross Entropy loss function:
* L = sum_i actual_i * log( predicted_i )
* Note: this is the same loss function as {@link LossMCXENT}, the only difference being the format for the labels -
* this loss function uses integer indices (zero indexed) for the loss array, whereas LossMCXENT uses the equivalent
* one-hot representation
*
* @author Alex Black
* @see LossNegativeLogLikelihood
* @see LossMCXENT
*/
@EqualsAndHashCode(callSuper = true)
@JsonInclude(JsonInclude.Include.NON_NULL)
@Getter @Setter
public class LossSparseMCXENT extends LossMCXENT {
private static final double DEFAULT_SOFTMAX_CLIPPING_EPSILON = 1e-10;
public LossSparseMCXENT() {
this(null);
}
/**
* Multi-Class Cross Entropy 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 LossSparseMCXENT(INDArray weights) {
this(DEFAULT_SOFTMAX_CLIPPING_EPSILON, weights);
}
/**
* Multi-Class Cross Entropy loss function where each the output is (optionally) weighted/scaled by a fixed 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 LossSparseMCXENT(@JsonProperty("softmaxClipEps") double softmaxClipEps, @JsonProperty("weights") INDArray weights) {
super(softmaxClipEps, weights);
}
protected INDArray sparseScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
INDArray oneHotLabels = toOneHot(labels, preOutput);
return super.scoreArray(oneHotLabels, preOutput, activationFn, mask);
}
@Override
public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask,
boolean average) {
INDArray oneHotLabels = toOneHot(labels, preOutput);
return super.computeScore(oneHotLabels, preOutput, activationFn, mask, average);
}
@Override
public INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
INDArray scoreArr = sparseScoreArray(labels, preOutput, activationFn, mask);
return scoreArr.sum(true,1).muli(-1);
}
@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
INDArray oneHotLabels = toOneHot(labels, preOutput);
return super.computeGradient(oneHotLabels, preOutput, activationFn, mask);
}
@Override
public Pair computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn,
INDArray mask, boolean average) {
INDArray oneHotLabels = toOneHot(labels, preOutput);
return new Pair<>(super.computeScore(oneHotLabels, preOutput, activationFn, mask, average),
super.computeGradient(oneHotLabels, preOutput, activationFn, mask));
}
private INDArray toOneHot(INDArray labels, INDArray preOutput){
Preconditions.checkState(labels.size(-1) == 1, "Labels for LossSparseMCXENT should be an array of integers " +
"with first dimension equal to minibatch size, and last dimension having size 1. Got labels array with shape %ndShape", labels);
INDArray oneHotLabels = preOutput.ulike();
Nd4j.exec(new OneHot(labels.reshape(labels.length()), oneHotLabels, (int)preOutput.size(-1)));
return oneHotLabels;
}
@Override
public String toString() {
if (weights == null)
return "LossSparseMCXENT()";
return "LossSparseMCXENT(weights=" + weights + ")";
}
}
