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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.deeplearning4j.earlystopping.scorecalc;

import org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.variational.VariationalAutoencoder;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;

/**
 * Score calculator for variational autoencoder reconstruction probability or reconstruction log probability for a
 * MultiLayerNetwork or ComputationGraph. VariationalAutoencoder layer must be first layer in the network
* See {@link VariationalAutoencoder#reconstructionProbability(INDArray, int)} for more details * * @author Alex Black */ public class VAEReconProbScoreCalculator extends BaseScoreCalculator { protected final int reconstructionProbNumSamples; protected final boolean logProb; protected final boolean average; /** * Constructor for average reconstruction probability * * @param iterator Iterator * @param reconstructionProbNumSamples Number of samples. See {@link VariationalAutoencoder#reconstructionProbability(INDArray, int)} * for details * @param logProb If true: calculate (negative) log probability. False: probability */ public VAEReconProbScoreCalculator(DataSetIterator iterator, int reconstructionProbNumSamples, boolean logProb) { this(iterator, reconstructionProbNumSamples, logProb, true); } /** * Constructor for reconstruction probability * * @param iterator Iterator * @param reconstructionProbNumSamples Number of samples. See {@link VariationalAutoencoder#reconstructionProbability(INDArray, int)} * for details * @param logProb If true: calculate (negative) log probability. False: probability * @param average If true: return average (log) probability. False: sum of log probability. * */ public VAEReconProbScoreCalculator(DataSetIterator iterator, int reconstructionProbNumSamples, boolean logProb, boolean average){ super(iterator); this.reconstructionProbNumSamples = reconstructionProbNumSamples; this.logProb = logProb; this.average = average; } @Override protected void reset() { scoreSum = 0; minibatchCount = 0; exampleCount = 0; } @Override protected INDArray output(Model network, INDArray input, INDArray fMask, INDArray lMask) { return null; //Not used } @Override protected INDArray[] output(Model network, INDArray[] input, INDArray[] fMask, INDArray[] lMask) { return null; //Not used } @Override protected double scoreMinibatch(Model net, INDArray features, INDArray labels, INDArray fMask, INDArray lMask, INDArray output) { Layer l; if(net instanceof MultiLayerNetwork) { MultiLayerNetwork network = (MultiLayerNetwork)net; l = network.getLayer(0); } else { ComputationGraph network = (ComputationGraph)net; l = network.getLayer(0); } if(!(l instanceof VariationalAutoencoder)){ throw new UnsupportedOperationException("Can only score networks with VariationalAutoencoder layers as first layer -" + " got " + l.getClass().getSimpleName()); } VariationalAutoencoder vae = (VariationalAutoencoder)l; //Reconstruction prob if(logProb){ return -vae.reconstructionLogProbability(features, reconstructionProbNumSamples).sumNumber().doubleValue(); } else { return vae.reconstructionProbability(features, reconstructionProbNumSamples).sumNumber().doubleValue(); } } @Override protected double scoreMinibatch(Model network, INDArray[] features, INDArray[] labels, INDArray[] fMask, INDArray[] lMask, INDArray[] output) { return 0; } @Override protected double finalScore(double scoreSum, int minibatchCount, int exampleCount) { if(average){ return scoreSum / exampleCount; } else { return scoreSum; } } @Override public boolean minimizeScore() { return false; //Maximize the reconstruction probability } }




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