117 lines
4.5 KiB
Java
117 lines
4.5 KiB
Java
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.earlystopping.scorecalc;
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import org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator;
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import org.deeplearning4j.nn.api.Model;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.dataset.MultiDataSet;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
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import org.nd4j.shade.jackson.annotation.JsonProperty;
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/** Given a DataSetIterator: calculate the total loss for the model on that data set.
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* Can be used for both MultiLayerNetwork and ComputationGraph
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*
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* @author Alex Black
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*/
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public class DataSetLossCalculator extends BaseScoreCalculator<Model> {
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@JsonProperty
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private boolean average;
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/**
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* Calculate the score (loss function value) on a given data set (usually a test set)
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*
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* @param dataSetIterator Data set to calculate the score for
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* @param average Whether to return the average (sum of loss / N) or just (sum of loss)
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*/
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public DataSetLossCalculator(DataSetIterator dataSetIterator, boolean average) {
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super(dataSetIterator);
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this.average = average;
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}
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/**Calculate the score (loss function value) on a given data set (usually a test set)
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*
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* @param dataSetIterator Data set to calculate the score for
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* @param average Whether to return the average (sum of loss / N) or just (sum of loss)
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*/
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public DataSetLossCalculator(MultiDataSetIterator dataSetIterator, boolean average) {
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super(dataSetIterator);
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this.average = average;
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}
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@Override
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public String toString() {
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return "DataSetLossCalculator(average=" + average + ")";
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}
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@Override
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protected void reset() {
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scoreSum = 0;
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minibatchCount = 0;
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exampleCount = 0;
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}
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@Override
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protected INDArray output(Model network, INDArray input, INDArray fMask, INDArray lMask) {
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return output(network, arr(input), arr(fMask), arr(lMask))[0];
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}
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@Override
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protected INDArray[] output(Model network, INDArray[] input, INDArray[] fMask, INDArray[] lMask) {
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if(network instanceof MultiLayerNetwork){
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INDArray out = ((MultiLayerNetwork) network).output(input[0], false, get0(fMask), get0(lMask));
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return new INDArray[]{out};
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} else if(network instanceof ComputationGraph){
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return ((ComputationGraph) network).output(false, input, fMask, lMask);
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} else {
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throw new RuntimeException("Unknown model type: " + network.getClass());
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}
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}
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@Override
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protected double scoreMinibatch(Model network, INDArray[] features, INDArray[] labels, INDArray[] fMask, INDArray[] lMask, INDArray[] output) {
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if(network instanceof MultiLayerNetwork){
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return ((MultiLayerNetwork) network).score(new DataSet(get0(features), get0(labels), get0(fMask), get0(lMask)), false)
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* features[0].size(0);
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} else if(network instanceof ComputationGraph){
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return ((ComputationGraph) network).score(new MultiDataSet(features, labels, fMask, lMask))
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* features[0].size(0);
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} else {
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throw new RuntimeException("Unknown model type: " + network.getClass());
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}
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}
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@Override
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protected double finalScore(double scoreSum, int minibatchCount, int exampleCount) {
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if(average){
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return scoreSum / exampleCount;
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} else {
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return scoreSum;
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}
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}
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@Override
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public boolean minimizeScore() {
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return true; //Minimize loss
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}
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}
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