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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
}
}