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February 9, 2016 15:52
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DBNMnistFullExample
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| import org.canova.api.split.FileSplit; | |
| import org.canova.image.recordreader.ImageRecordReader; | |
| import org.deeplearning4j.datasets.canova.RecordReaderDataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.DataSetIterator; | |
| import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; | |
| import org.deeplearning4j.eval.Evaluation; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.GradientNormalization; | |
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.layers.OutputLayer; | |
| import org.deeplearning4j.nn.conf.layers.RBM; | |
| import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; | |
| import org.deeplearning4j.nn.weights.WeightInit; | |
| import org.deeplearning4j.optimize.api.IterationListener; | |
| import org.deeplearning4j.optimize.listeners.ScoreIterationListener; | |
| import org.nd4j.linalg.api.ndarray.INDArray; | |
| import org.nd4j.linalg.dataset.DataSet; | |
| import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; | |
| import org.slf4j.Logger; | |
| import org.slf4j.LoggerFactory; | |
| import java.awt.image.BufferedImage; | |
| import java.io.File; | |
| import java.util.Collections; | |
| /** | |
| * Created by agibsonccc on 9/11/14. | |
| */ | |
| public class DBNMnistFullExample { | |
| /** DOCUMENT ME! */ | |
| private static Logger log = LoggerFactory.getLogger( DBNMnistFullExample.class ); | |
| /** | |
| * DOCUMENT ME! | |
| * | |
| * @param args DOCUMENT ME! | |
| * | |
| * @throws Exception DOCUMENT ME! | |
| */ | |
| public static void main( String[] args ) throws Exception { | |
| final int numRows = 20; | |
| final int numColumns = 20; | |
| int outputNum = 10; | |
| int numSamples = 60000; | |
| int batchSize = numRows * numColumns; | |
| int batchRows = batchSize; | |
| int iterations = 10; | |
| int seed = 123; | |
| int listenerFreq = batchSize / 5; | |
| log.info( "Load data...." ); | |
| //DataSetIterator iter = new MnistDataSetIterator( batchSize, numSamples, true ); | |
| ImageRecordReader imageRecordReader = new ImageRecordReader( numRows, numColumns, BufferedImage.TYPE_BYTE_GRAY, | |
| true ); | |
| imageRecordReader.initialize( new FileSplit( new File( "src/test/resources/data/" ) ) ); | |
| //DataSetIterator iter = new MnistDataSetIterator( batchSize, numSamples, true ); | |
| DataSetIterator iter = new RecordReaderDataSetIterator( imageRecordReader, batchSize ); | |
| log.info( "Build model...." ); | |
| MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed( seed ) | |
| .gradientNormalization( | |
| GradientNormalization.ClipElementWiseAbsoluteValue ) | |
| .gradientNormalizationThreshold( 1.0 ) | |
| .iterations( iterations ).momentum( 0.5 ) | |
| .momentumAfter( Collections.singletonMap( 3, | |
| 0.9 ) ) | |
| .optimizationAlgo( | |
| OptimizationAlgorithm.CONJUGATE_GRADIENT ) | |
| .list( 4 ) | |
| .layer( 0, new RBM.Builder().nIn( | |
| batchSize ) | |
| .nOut( | |
| batchRows ) | |
| .weightInit( | |
| WeightInit.XAVIER ) | |
| .lossFunction( | |
| LossFunction.RMSE_XENT ) | |
| .visibleUnit( | |
| RBM | |
| .VisibleUnit.BINARY ) | |
| .hiddenUnit( | |
| RBM | |
| .HiddenUnit.BINARY ) | |
| .build() ) | |
| .layer( 1, new RBM.Builder().nIn( | |
| batchRows ) | |
| .nOut( 250 ) | |
| .weightInit( | |
| WeightInit.XAVIER ) | |
| .lossFunction( | |
| LossFunction.RMSE_XENT ) | |
| .visibleUnit( | |
| RBM | |
| .VisibleUnit.BINARY ) | |
| .hiddenUnit( | |
| RBM | |
| .HiddenUnit.BINARY ) | |
| .build() ) | |
| .layer( 2, new RBM.Builder().nIn( 250 ) | |
| .nOut( 200 ) | |
| .weightInit( | |
| WeightInit.XAVIER ) | |
| .lossFunction( | |
| LossFunction.RMSE_XENT ) | |
| .visibleUnit( | |
| RBM | |
| .VisibleUnit.BINARY ) | |
| .hiddenUnit( | |
| RBM | |
| .HiddenUnit.BINARY ) | |
| .build() ) | |
| .layer( 3, | |
| new OutputLayer.Builder( | |
| LossFunction.NEGATIVELOGLIKELIHOOD ) | |
| .activation( "softmax" ).nIn( 200 ).nOut( outputNum ).build() ).pretrain( true ).backprop( false ) | |
| .build(); | |
| MultiLayerNetwork model = new MultiLayerNetwork( conf ); | |
| model.init(); | |
| model.setListeners( Collections.singletonList( | |
| ( IterationListener )new ScoreIterationListener( listenerFreq ) ) ); | |
| log.info( "Train model...." ); | |
| model.fit( iter ); // achieves end to end pre-training | |
| log.info( "Evaluate model...." ); | |
| Evaluation eval = new Evaluation( outputNum ); | |
| DataSetIterator testIter = new MnistDataSetIterator( 100, 10000 ); | |
| while( testIter.hasNext() ) { | |
| DataSet testMnist = testIter.next(); | |
| INDArray predict2 = model.output( testMnist.getFeatureMatrix() ); | |
| eval.eval( testMnist.getLabels(), predict2 ); | |
| } | |
| log.info( eval.stats() ); | |
| log.info( "****************Example finished********************" ); | |
| } | |
| } |
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