List of usage examples for org.deeplearning4j.optimize.listeners ScoreIterationListener ScoreIterationListener
public ScoreIterationListener(int printIterations)
From source file:aiLogicImplementation.RNNBasic.java
License:Apache License
public void create(StatsStorage statsStorage) { NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder(); builder.iterations(1000);/*from w w w.j a v a 2 s . com*/ builder.learningRate(0.01); builder.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT); builder.seed(123); builder.biasInit(0.2); builder.miniBatch(true); // builder.updater(Updater.NESTEROVS); builder.updater(new Nesterovs(0.01)); builder.weightInit(WeightInit.XAVIER); builder.regularization(true); builder.l2(0.001); // builder.gradientNormalization(GradientNormalization.ClipL2PerParamType); // builder.gradientNormalizationThreshold(0.5); ListBuilder listBuilder = builder.list(); for (int i = 0; i < HIDDEN_LAYER_CONT; i++) { GravesLSTM.Builder hiddenLayerBuilder = new GravesLSTM.Builder(); hiddenLayerBuilder.nIn(i == 0 ? NUMBER_OF_FEATURE_INPUT : HIDDEN_LAYER_WIDTH); hiddenLayerBuilder.nOut(HIDDEN_LAYER_WIDTH); if (i == 0) { hiddenLayerBuilder.activation(Activation.TANH); } else { hiddenLayerBuilder.activation(Activation.TANH); } listBuilder.layer(i, hiddenLayerBuilder.build()); } // RnnOutputLayer.Builder outputLayerBuilder = new RnnOutputLayer.Builder(LossFunction.XENT); RnnOutputLayer.Builder outputLayerBuilder = new RnnOutputLayer.Builder(LossFunction.L2); // RnnOutputLayer.Builder outputLayerBuilder = new RnnOutputLayer.Builder(LossFunction.MCXENT); // outputLayerBuilder.activation(Activation.SIGMOID); outputLayerBuilder.activation(Activation.RELU); // outputLayerBuilder.activation(Activation.SOFTMAX); outputLayerBuilder.nIn(HIDDEN_LAYER_WIDTH); outputLayerBuilder.nOut(NUMBER_OF_FEATURE_OUTPUT); outputLayerBuilder.weightInit(WeightInit.XAVIER); // outputLayerBuilder.dist(new UniformDistribution(0, 1)); listBuilder.layer(HIDDEN_LAYER_CONT, outputLayerBuilder.build()); // listBuilder.backpropType(BackpropType.TruncatedBPTT); // listBuilder.tBPTTForwardLength(tbpttLength); // listBuilder.tBPTTBackwardLength(tbpttLength); listBuilder.pretrain(false); listBuilder.backprop(true); // create network MultiLayerConfiguration conf = listBuilder.build(); net = new MultiLayerNetwork(conf); net.init(); // net.setListeners(new ScoreIterationListener(configuration.getListenerFrequency())); //Then add the StatsListener to collect this information from the network, as it trains List list = new ArrayList(); list.add(new StatsListener(statsStorage, configuration.getListenerFrequency())); list.add(new ScoreIterationListener(configuration.getListenerFrequency())); net.setListeners(list); /* * CREATE OUR TRAINING DATA */ input = Nd4j.zeros(maze.length * maze.length * hibertMaze.getHibertMazeGUI().getAlPlayers().size() * hibertMaze.getHibertMazeGUI().getAlFoods().size(), NUMBER_OF_FEATURE_INPUT); labels = Nd4j.zeros(maze.length * maze.length * hibertMaze.getHibertMazeGUI().getAlPlayers().size() * hibertMaze.getHibertMazeGUI().getAlFoods().size(), NUMBER_OF_FEATURE_OUTPUT); positionMoving = new PositionMoving(maze); int counter = 0; for (int i = 0; i < maze.length; i++) { for (int j = 0; j < maze[i].length; j++) { int mazeFieldValue = maze[i][j]; // input neuron for current-char is 1 at "samplePos" for (Player player : hibertMaze.getHibertMazeGUI().getAlPlayers()) { for (Food food : hibertMaze.getHibertMazeGUI().getAlFoods()) { Position position = new Position(i, j); input.putScalar(counter, 0, i); input.putScalar(counter, 1, j); input.putScalar(counter, 2, mazeFieldValue); input.putScalar(counter, 3, player.getPlayerNumber()); input.putScalar(counter, 4, i); input.putScalar(counter, 5, j); input.putScalar(counter, 6, food.getFoodNumber()); input.putScalar(counter, 7, food.getPosition().getX()); input.putScalar(counter, 8, food.getPosition().getY()); input.putScalar(counter, 9, positionMoving.moveNorth(position) ? 1.00d : 0.00d); input.putScalar(counter, 10, positionMoving.moveSouth(position) ? 1.00d : 0.00d); input.putScalar(counter, 11, positionMoving.moveEast(position) ? 1.00d : 0.00d); input.putScalar(counter, 12, positionMoving.moveWest(position) ? 1.00d : 0.00d); input.putScalar(counter, 13, player.getPosition().getX() == food.getPosition().getX() && player.getPosition().getY() == food.getPosition().getY() ? 1.00d : 0.00d); labels.putScalar(counter, 0, positionMoving.moveNorth(position) ? 1.00d : 0.00d); labels.putScalar(counter, 1, positionMoving.moveSouth(position) ? 1.00d : 0.00d); labels.putScalar(counter, 2, positionMoving.moveEast(position) ? 1.00d : 0.00d); labels.putScalar(counter, 3, positionMoving.moveWest(position) ? 1.00d : 0.00d); counter = counter + 1; } } } } trainingData = new DataSet(input, labels); }
From source file:cnn.image.classification.CNNImageClassification.java
public static void main(String[] args) { int nChannels = 3; int outputNum = 10; // int numExamples = 80; int batchSize = 10; int nEpochs = 20; int iterations = 1; int seed = 123; int height = 32; int width = 32; Random randNumGen = new Random(seed); System.out.println("Load data...."); File parentDir = new File("train1/"); FileSplit filesInDir = new FileSplit(parentDir, allowedExtensions, randNumGen); ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator(); BalancedPathFilter pathFilter = new BalancedPathFilter(randNumGen, allowedExtensions, labelMaker); //Split the image files into train and test. Specify the train test split as 80%,20% InputSplit[] filesInDirSplit = filesInDir.sample(pathFilter, 100, 0); InputSplit[] filesInDirSplitTest = filesInDir.sample(pathFilter, 0, 100); InputSplit trainData = filesInDirSplit[0]; InputSplit testData = filesInDirSplitTest[1]; System.out.println("train = " + trainData.length()); System.out.println("test = " + testData.length()); //Specifying a new record reader with the height and width you want the images to be resized to. //Note that the images in this example are all of different size //They will all be resized to the height and width specified below ImageRecordReader recordReader = new ImageRecordReader(height, width, nChannels, labelMaker); //Often there is a need to transforming images to artificially increase the size of the dataset recordReader.initialize(trainData);/*from w w w . j a v a 2 s. co m*/ DataSetIterator dataIterTrain = new RecordReaderDataSetIterator(recordReader, batchSize, 1, outputNum); // recordReader.reset(); recordReader.initialize(testData); DataSetIterator dataIterTest = new RecordReaderDataSetIterator(recordReader, batchSize, 1, outputNum); DataNormalization scaler = new ImagePreProcessingScaler(0, 1); dataIterTrain.setPreProcessor(scaler); dataIterTest.setPreProcessor(scaler); System.out.println("Build model...."); MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed) .iterations(iterations).regularization(true).l2(0.0005) // .dropOut(0.5) .learningRate(0.001)//.biasLearningRate(0.02) //.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75) .weightInit(WeightInit.XAVIER).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(Updater.NESTEROVS).momentum(0.9).list() .layer(0, new ConvolutionLayer.Builder(5, 5).nIn(nChannels).stride(1, 1).nOut(20) .activation("identity").build()) .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2) .build()) .layer(2, new ConvolutionLayer.Builder(5, 5).stride(1, 1).nOut(50).activation("identity").build()) .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2).stride(2, 2).build()) .layer(4, new DenseLayer.Builder().activation("relu").nOut(500).build()) .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum) .activation("softmax").build()) .setInputType(InputType.convolutional(height, width, nChannels)) //See note below .backprop(true).pretrain(false); MultiLayerConfiguration b = new NeuralNetConfiguration.Builder().seed(seed).iterations(iterations) .regularization(false).l2(0.005) // tried 0.0001, 0.0005 .learningRate(0.0001) // tried 0.00001, 0.00005, 0.000001 .weightInit(WeightInit.XAVIER).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(Updater.NESTEROVS).momentum(0.9).list().layer(0, new ConvolutionLayer.Builder(5, 5) //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied .nIn(nChannels).stride(1, 1).nOut(50) // tried 10, 20, 40, 50 .activation("relu").build()) .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2).stride(2, 2).build()) .layer(2, new ConvolutionLayer.Builder(5, 5).stride(1, 1).nOut(100) // tried 25, 50, 100 .activation("relu").build()) .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2).stride(2, 2).build()) .layer(4, new DenseLayer.Builder().activation("relu").nOut(500).build()) .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum) .activation("softmax").build()) .backprop(true).pretrain(false).cnnInputSize(height, width, nChannels).build(); MultiLayerConfiguration conf = builder.build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); System.out.println("Train model...."); model.setListeners(new ScoreIterationListener(1)); // for( int i=0; i<nEpochs; i++ ) { // model.setListeners(new HistogramIterationListener(1)); MultipleEpochsIterator trainIter = new MultipleEpochsIterator(nEpochs, dataIterTrain, 2); model.fit(trainIter); // System.out.println("*** Completed epoch - " + i + " ***"); System.out.println("Evaluate model...."); // Evaluation eval = new Evaluation(outputNum); // while(dataIterTest.hasNext()){ // DataSet ds = dataIterTest.next(); // INDArray output = model.output(ds.getFeatureMatrix(), false); // eval.eval(ds.getLabels(), output); // } // System.out.println(eval.stats()); // dataIterTest.reset(); // } Evaluation eval1 = model.evaluate(dataIterTest); System.out.println(eval1.stats()); System.out.println("****************Example finished********************"); }
From source file:com.example.android.displayingbitmaps.ui.ImageGridActivity.java
License:Apache License
public void trainMLP() throws Exception { Nd4j.ENFORCE_NUMERICAL_STABILITY = true; final int numRows = 28; final int numColumns = 28; int outputNum = 10; int numSamples = 10000; int batchSize = 500; int iterations = 10; int seed = 123; int listenerFreq = iterations / 5; int splitTrainNum = (int) (batchSize * .8); DataSet mnist;//from w w w . j av a 2 s . c o m SplitTestAndTrain trainTest; DataSet trainInput; List<INDArray> testInput = new ArrayList<>(); List<INDArray> testLabels = new ArrayList<>(); log.info("Load data...."); DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true); log.info("Build model...."); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(iterations) .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).learningRate(1e-1f) .momentum(0.5).momentumAfter(Collections.singletonMap(3, 0.9)).useDropConnect(true).list(2) .layer(0, new DenseLayer.Builder().nIn(numRows * numColumns).nOut(1000).activation("relu") .weightInit(WeightInit.XAVIER).build()) .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD).nIn(1000).nOut(outputNum) .activation("softmax").weightInit(WeightInit.XAVIER).build()) .build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq))); log.info("Train model...."); model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq))); while (mnistIter.hasNext()) { mnist = mnistIter.next(); trainTest = mnist.splitTestAndTrain(splitTrainNum, new Random(seed)); // train set that is the result trainInput = trainTest.getTrain(); // get feature matrix and labels for training testInput.add(trainTest.getTest().getFeatureMatrix()); testLabels.add(trainTest.getTest().getLabels()); model.fit(trainInput); } log.info("Evaluate model...."); Evaluation eval = new Evaluation(outputNum); for (int i = 0; i < testInput.size(); i++) { INDArray output = model.output(testInput.get(i)); eval.eval(testLabels.get(i), output); } log.info(eval.stats()); log.info("****************Example finished********************"); }
From source file:com.heatonresearch.aifh.examples.ann.LearnAutoMPGBackprop.java
License:Apache License
/** * The main method./*from w w w .j a va 2 s. c om*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 0.01; // Setup training data. final InputStream istream = LearnAutoMPGBackprop.class.getResourceAsStream("/auto-mpg.data.csv"); if (istream == null) { System.out.println("Cannot access data set, make sure the resources are available."); System.exit(1); } final NormalizeDataSet ds = NormalizeDataSet.load(istream); istream.close(); // The following ranges are setup for the Auto MPG data set. If you wish to normalize other files you will // need to modify the below function calls other files. // First remove some columns that we will not use: ds.deleteColumn(8); // Car name ds.deleteColumn(7); // Car origin ds.deleteColumn(6); // Year ds.deleteUnknowns(); ds.normalizeZScore(1); ds.normalizeZScore(2); ds.normalizeZScore(3); ds.normalizeZScore(4); ds.normalizeZScore(5); DataSet next = ds.extractSupervised(1, 4, 0, 1); next.shuffle(); // Training and validation data split int splitTrainNum = (int) (next.numExamples() * .75); SplitTestAndTrain testAndTrain = next.splitTestAndTrain(splitTrainNum, new Random(seed)); DataSet trainSet = testAndTrain.getTrain(); DataSet validationSet = testAndTrain.getTest(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainSet.asList(), trainSet.numExamples()); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationSet.numExamples()); // Create neural network. int numInputs = next.numInputs(); int numOutputs = next.numOutcomes(); int numHiddenNodes = 50; MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.MSE).weightInit(WeightInit.XAVIER) .activation("identity").nIn(numHiddenNodes).nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() .epochTerminationConditions(new MaxEpochsTerminationCondition(500)) //Max of 50 epochs .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(25)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); System.out.println(features + ":Prediction(" + predicted + "):Actual(" + labels + ")"); } } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnDigitsBackprop.java
License:Apache License
/** * The main method./*w ww. ja va 2 s . c om*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 1e-2; int nEpochs = 50; int batchSize = 500; // Setup training data. System.out.println("Please wait, reading MNIST training data."); String dir = System.getProperty("user.dir"); MNISTReader trainingReader = MNIST.loadMNIST(dir, true); MNISTReader validationReader = MNIST.loadMNIST(dir, false); DataSet trainingSet = trainingReader.getData(); DataSet validationSet = validationReader.getData(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows()); System.out.println("Training set size: " + trainingReader.getNumImages()); System.out.println("Validation set size: " + validationReader.getNumImages()); System.out.println(trainingSet.get(0).getFeatures().size(1)); System.out.println(validationSet.get(0).getFeatures().size(1)); int numInputs = trainingReader.getNumCols() * trainingReader.getNumRows(); int numOutputs = 10; int numHiddenNodes = 200; // Create neural network. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).regularization(true).dropOut(0.50).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) .weightInit(WeightInit.XAVIER).activation("softmax").nIn(numHiddenNodes) .nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() //.epochTerminationConditions(new MaxEpochsTerminationCondition(10)) .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnDigitsConv.java
License:Apache License
/** * The main method./*from w w w . j a v a 2 s .co m*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 1e-2; int nEpochs = 50; int batchSize = 500; int channels = 1; // Setup training data. System.out.println("Please wait, reading MNIST training data."); String dir = System.getProperty("user.dir"); MNISTReader trainingReader = MNIST.loadMNIST(dir, true); MNISTReader validationReader = MNIST.loadMNIST(dir, false); DataSet trainingSet = trainingReader.getData(); DataSet validationSet = validationReader.getData(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows()); System.out.println("Training set size: " + trainingReader.getNumImages()); System.out.println("Validation set size: " + validationReader.getNumImages()); int numOutputs = 10; // Create neural network. MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .regularization(true).l2(0.0005).learningRate(0.01).weightInit(WeightInit.XAVIER) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.NESTEROVS) .momentum(0.9).list(4) .layer(0, new ConvolutionLayer.Builder(5, 5).nIn(channels).stride(1, 1).nOut(20).dropOut(0.5) .activation("relu").build()) .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2) .stride(2, 2).build()) .layer(2, new DenseLayer.Builder().activation("relu").nOut(500).build()) .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10) .activation("softmax").build()) .backprop(true).pretrain(false); new ConvolutionLayerSetup(builder, 28, 28, 1); MultiLayerConfiguration conf = builder.build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() //.epochTerminationConditions(new MaxEpochsTerminationCondition(10)) .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnDigitsDropout.java
License:Apache License
/** * The main method.//from w w w. ja v a2 s.c om * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 1e-2; int nEpochs = 50; int batchSize = 500; // Setup training data. System.out.println("Please wait, reading MNIST training data."); String dir = System.getProperty("user.dir"); MNISTReader trainingReader = MNIST.loadMNIST(dir, true); MNISTReader validationReader = MNIST.loadMNIST(dir, false); DataSet trainingSet = trainingReader.getData(); DataSet validationSet = validationReader.getData(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows()); System.out.println("Training set size: " + trainingReader.getNumImages()); System.out.println("Validation set size: " + validationReader.getNumImages()); System.out.println(trainingSet.get(0).getFeatures().size(1)); System.out.println(validationSet.get(0).getFeatures().size(1)); int numInputs = trainingReader.getNumCols() * trainingReader.getNumRows(); int numOutputs = 10; int numHiddenNodes = 100; // Create neural network. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) .weightInit(WeightInit.XAVIER).activation("softmax").nIn(numHiddenNodes) .nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() //.epochTerminationConditions(new MaxEpochsTerminationCondition(10)) .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnIrisBackprop.java
License:Apache License
/** * The main method./*from w w w. j ava 2 s . c o m*/ * @param args Not used. */ public static void main(String[] args) { try { int seed = 43; double learningRate = 0.1; int splitTrainNum = (int) (150 * .75); int numInputs = 4; int numOutputs = 3; int numHiddenNodes = 50; // Setup training data. final InputStream istream = LearnIrisBackprop.class.getResourceAsStream("/iris.csv"); if (istream == null) { System.out.println("Cannot access data set, make sure the resources are available."); System.exit(1); } final NormalizeDataSet ds = NormalizeDataSet.load(istream); final CategoryMap species = ds.encodeOneOfN(4); // species is column 4 istream.close(); DataSet next = ds.extractSupervised(0, 4, 4, 3); next.shuffle(); // Training and validation data split SplitTestAndTrain testAndTrain = next.splitTestAndTrain(splitTrainNum, new Random(seed)); DataSet trainSet = testAndTrain.getTrain(); DataSet validationSet = testAndTrain.getTest(); DataSetIterator trainSetIterator = new ListDataSetIterator(trainSet.asList(), trainSet.numExamples()); DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationSet.numExamples()); // Create neural network. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) .weightInit(WeightInit.XAVIER).activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) .weightInit(WeightInit.XAVIER).activation("softmax").nIn(numHiddenNodes) .nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Define when we want to stop training. EarlyStoppingModelSaver saver = new InMemoryModelSaver(); EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder() .epochTerminationConditions(new MaxEpochsTerminationCondition(500)) //Max of 50 epochs .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(25)) .evaluateEveryNEpochs(1).scoreCalculator(new DataSetLossCalculator(validationSetIterator, true)) //Calculate test set score .modelSaver(saver).build(); EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator); // Train and display result. EarlyStoppingResult result = trainer.fit(); System.out.println("Termination reason: " + result.getTerminationReason()); System.out.println("Termination details: " + result.getTerminationDetails()); System.out.println("Total epochs: " + result.getTotalEpochs()); System.out.println("Best epoch number: " + result.getBestModelEpoch()); System.out.println("Score at best epoch: " + result.getBestModelScore()); model = saver.getBestModel(); // Evaluate Evaluation eval = new Evaluation(numOutputs); validationSetIterator.reset(); for (int i = 0; i < validationSet.numExamples(); i++) { DataSet t = validationSet.get(i); INDArray features = t.getFeatureMatrix(); INDArray labels = t.getLabels(); INDArray predicted = model.output(features, false); System.out.println(features + ":Prediction(" + findSpecies(labels, species) + "):Actual(" + findSpecies(predicted, species) + ")" + predicted); eval.eval(labels, predicted); } //Print the evaluation statistics System.out.println(eval.stats()); } catch (Exception ex) { ex.printStackTrace(); } }
From source file:com.heatonresearch.aifh.examples.ann.LearnXORBackprop.java
License:Apache License
/** * The main method.//from w w w . j a v a2 s . c om * @param args Not used. */ public static void main(String[] args) { int seed = 43; double learningRate = 0.4; int nEpochs = 100; int numInputs = XOR_INPUT[0].length; int numOutputs = XOR_IDEAL[0].length; int numHiddenNodes = 4; // Setup training data. INDArray xorInput = Nd4j.create(XOR_INPUT); INDArray xorIdeal = Nd4j.create(XOR_IDEAL); DataSet xorDataSet = new DataSet(xorInput, xorIdeal); // Create neural network. MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(learningRate) .updater(Updater.NESTEROVS).momentum(0.9).list(2) .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes).weightInit(WeightInit.XAVIER) .activation("relu").build()) .layer(1, new OutputLayer.Builder(LossFunction.MSE).weightInit(WeightInit.XAVIER) .activation("identity").nIn(numHiddenNodes).nOut(numOutputs).build()) .pretrain(false).backprop(true).build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); model.setListeners(new ScoreIterationListener(1)); // Train for (int n = 0; n < nEpochs; n++) { model.fit(xorDataSet); } // Evaluate System.out.println("Evaluating neural network."); for (int i = 0; i < 4; i++) { INDArray input = xorInput.getRow(i); INDArray output = model.output(input); System.out.println(input + " : " + output); } }
From source file:com.javafxpert.neuralnetviz.scenario.PebbleGestures.java
License:Apache License
public static void main(String[] args) throws Exception { //First: get the dataset using the record reader. CSVRecordReader handles loading/parsing int numLinesToSkip = 0; String delimiter = ","; int miniBatchSize = 10; int numPossibleLabels = 3; int labelIndex = 3; boolean regression = false; final int numInputs = 3; int iterations = 600; long seed = 6; double learningRate = 0.003; int lstmLayerSize = 20; //Number of units in each GravesLSTM layer SequenceRecordReader reader = new CSVSequenceRecordReader(0, ","); reader.initialize(new NumberedFileInputSplit("src/main/resources/classification/pebble_data_%d.csv", 0, 2)); DataSetIterator variableLengthIter = new SequenceRecordReaderDataSetIterator(reader, miniBatchSize, numPossibleLabels, labelIndex, regression); //org.datavec.api.records.reader.RecordReader recordReader = new org.datavec.api.records.reader.impl.csv.CSVRecordReader(numLinesToSkip,delimiter); //recordReader.initialize(new org.datavec.api.split.FileSplit(new File("src/main/resources/classification/speed_dating_all.csv"))); DataSet allData = variableLengthIter.next(); //SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.99); //Use 65% of data for training //DataSet trainingData = testAndTrain.getTrain(); //DataSet testData = testAndTrain.getTest(); //We need to normalize our data. We'll use NormalizeStandardize (which gives us mean 0, unit variance): DataNormalization normalizer = new NormalizerStandardize(); normalizer.fit(allData); //Collect the statistics (mean/stdev) from the training data. This does not modify the input data normalizer.transform(allData); //Apply normalization to the training data //normalizer.transform(testData); //Apply normalization to the test data. This is using statistics calculated from the *training* set //Set up network configuration: MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(iterations) .learningRate(learningRate).rmsDecay(0.95).seed(12345).regularization(true).l2(0.001) .weightInit(WeightInit.XAVIER).updater(Updater.RMSPROP).list() .layer(0, new GravesLSTM.Builder().nIn(numInputs).nOut(lstmLayerSize).activation("tanh").build()) .layer(1,/*from w ww . j a v a 2 s.c om*/ new GravesLSTM.Builder().nIn(lstmLayerSize).nOut(lstmLayerSize).activation("tanh").build()) .layer(2, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation("softmax") //MCXENT + softmax for classification .nIn(lstmLayerSize).nOut(numPossibleLabels).build()) //.backpropType(BackpropType.TruncatedBPTT).tBPTTForwardLength(tbpttLength).tBPTTBackwardLength(tbpttLength) .pretrain(false).backprop(true).build(); String[] inputFeatureNames = { "accel x", "accel y", "accel z" }; String[] outputLabelNames = { "Subject A", "Subject B", "Subject C" }; MultiLayerNetworkEnhanced model = new MultiLayerNetworkEnhanced(conf, inputFeatureNames, outputLabelNames); model.init(); model.setListeners(new ScoreIterationListener(1)); //Print score every 1 parameter updates //model.setListeners(new ModelListener(10, webSocketSession)); model.setDataNormalization(normalizer); model.fit(allData); //evaluate the model on the test set /* Evaluation eval = new Evaluation(numPossibleLabels); INDArray output = model.output(testData.getFeatureMatrix()); eval.eval(testData.getLabels(), output); System.out.println(eval.stats()); */ // Make prediction // Expected output: 0 INDArray example = Nd4j.zeros(1, 3, 10); example.putScalar(new int[] { 0, 0, 0 }, 1895); example.putScalar(new int[] { 0, 1, 0 }, 2390); example.putScalar(new int[] { 0, 2, 0 }, 2024); example.putScalar(new int[] { 0, 0, 1 }, 1889); example.putScalar(new int[] { 0, 1, 1 }, 2389); example.putScalar(new int[] { 0, 2, 1 }, 2022); example.putScalar(new int[] { 0, 0, 2 }, 1886); example.putScalar(new int[] { 0, 1, 2 }, 2383); example.putScalar(new int[] { 0, 2, 2 }, 2027); example.putScalar(new int[] { 0, 0, 3 }, 1888); example.putScalar(new int[] { 0, 1, 3 }, 2382); example.putScalar(new int[] { 0, 2, 3 }, 2028); example.putScalar(new int[] { 0, 0, 4 }, 1889); example.putScalar(new int[] { 0, 1, 4 }, 2385); example.putScalar(new int[] { 0, 2, 4 }, 2027); example.putScalar(new int[] { 0, 0, 5 }, 1892); example.putScalar(new int[] { 0, 1, 5 }, 2386); example.putScalar(new int[] { 0, 2, 5 }, 2023); example.putScalar(new int[] { 0, 0, 6 }, 1893); example.putScalar(new int[] { 0, 1, 6 }, 2381); example.putScalar(new int[] { 0, 2, 6 }, 2020); example.putScalar(new int[] { 0, 0, 7 }, 1897); example.putScalar(new int[] { 0, 1, 7 }, 2388); example.putScalar(new int[] { 0, 2, 7 }, 2030); example.putScalar(new int[] { 0, 0, 8 }, 1893); example.putScalar(new int[] { 0, 1, 8 }, 2384); example.putScalar(new int[] { 0, 2, 8 }, 2027); example.putScalar(new int[] { 0, 0, 9 }, 1894); example.putScalar(new int[] { 0, 1, 9 }, 2387); example.putScalar(new int[] { 0, 2, 9 }, 2030); DataSet ds = new DataSet(example, null); normalizer.transform(ds); model.rnnClearPreviousState(); INDArray outputActivations = model.output(example); System.out.println("outputActivations expected 0: " + outputActivations); // Make prediction // Expected output: 1 example = Nd4j.zeros(1, 3, 10); example.putScalar(new int[] { 0, 0, 0 }, 2121); example.putScalar(new int[] { 0, 1, 0 }, 2349); example.putScalar(new int[] { 0, 2, 0 }, 1966); example.putScalar(new int[] { 0, 0, 1 }, 2124); example.putScalar(new int[] { 0, 1, 1 }, 2354); example.putScalar(new int[] { 0, 2, 1 }, 1966); example.putScalar(new int[] { 0, 0, 2 }, 2122); example.putScalar(new int[] { 0, 1, 2 }, 2357); example.putScalar(new int[] { 0, 2, 2 }, 1970); example.putScalar(new int[] { 0, 0, 3 }, 2122); example.putScalar(new int[] { 0, 1, 3 }, 2355); example.putScalar(new int[] { 0, 2, 3 }, 1966); example.putScalar(new int[] { 0, 0, 4 }, 2123); example.putScalar(new int[] { 0, 1, 4 }, 2347); example.putScalar(new int[] { 0, 2, 4 }, 1971); example.putScalar(new int[] { 0, 0, 5 }, 2123); example.putScalar(new int[] { 0, 1, 5 }, 2347); example.putScalar(new int[] { 0, 2, 5 }, 1967); example.putScalar(new int[] { 0, 0, 6 }, 2119); example.putScalar(new int[] { 0, 1, 6 }, 2354); example.putScalar(new int[] { 0, 2, 6 }, 1966); example.putScalar(new int[] { 0, 0, 7 }, 2114); example.putScalar(new int[] { 0, 1, 7 }, 2350); example.putScalar(new int[] { 0, 2, 7 }, 1963); example.putScalar(new int[] { 0, 0, 8 }, 2123); example.putScalar(new int[] { 0, 1, 8 }, 2351); example.putScalar(new int[] { 0, 2, 8 }, 1966); example.putScalar(new int[] { 0, 0, 9 }, 2126); example.putScalar(new int[] { 0, 1, 9 }, 2351); example.putScalar(new int[] { 0, 2, 9 }, 1963); ds = new DataSet(example, null); normalizer.transform(ds); model.rnnClearPreviousState(); outputActivations = model.output(example); System.out.println("outputActivations expected 1: " + outputActivations); // Make prediction // Expected output: 2 example = Nd4j.zeros(1, 3, 10); example.putScalar(new int[] { 0, 0, 0 }, 1925); example.putScalar(new int[] { 0, 1, 0 }, 2386); example.putScalar(new int[] { 0, 2, 0 }, 1983); example.putScalar(new int[] { 0, 0, 1 }, 1925); example.putScalar(new int[] { 0, 1, 1 }, 2389); example.putScalar(new int[] { 0, 2, 1 }, 1983); example.putScalar(new int[] { 0, 0, 2 }, 1923); example.putScalar(new int[] { 0, 1, 2 }, 2393); example.putScalar(new int[] { 0, 2, 2 }, 1985); example.putScalar(new int[] { 0, 0, 3 }, 1918); example.putScalar(new int[] { 0, 1, 3 }, 2386); example.putScalar(new int[] { 0, 2, 3 }, 1980); example.putScalar(new int[] { 0, 0, 4 }, 1922); example.putScalar(new int[] { 0, 1, 4 }, 2393); example.putScalar(new int[] { 0, 2, 4 }, 1978); example.putScalar(new int[] { 0, 0, 5 }, 1918); example.putScalar(new int[] { 0, 1, 5 }, 2383); example.putScalar(new int[] { 0, 2, 5 }, 1987); example.putScalar(new int[] { 0, 0, 6 }, 1927); example.putScalar(new int[] { 0, 1, 6 }, 2385); example.putScalar(new int[] { 0, 2, 6 }, 1984); example.putScalar(new int[] { 0, 0, 7 }, 1927); example.putScalar(new int[] { 0, 1, 7 }, 2384); example.putScalar(new int[] { 0, 2, 7 }, 1986); example.putScalar(new int[] { 0, 0, 8 }, 1922); example.putScalar(new int[] { 0, 1, 8 }, 2391); example.putScalar(new int[] { 0, 2, 8 }, 1985); example.putScalar(new int[] { 0, 0, 9 }, 1919); example.putScalar(new int[] { 0, 1, 9 }, 2389); example.putScalar(new int[] { 0, 2, 9 }, 1986); ds = new DataSet(example, null); normalizer.transform(ds); model.rnnClearPreviousState(); outputActivations = model.output(example); System.out.println("outputActivations expected 2: " + outputActivations); // Make prediction // Expected output: 2 example = Nd4j.zeros(1, 3, 10); example.putScalar(new int[] { 0, 0, 0 }, 1923); example.putScalar(new int[] { 0, 1, 0 }, 2335); example.putScalar(new int[] { 0, 2, 0 }, 1953); example.putScalar(new int[] { 0, 0, 1 }, 1945); example.putScalar(new int[] { 0, 1, 1 }, 2382); example.putScalar(new int[] { 0, 2, 1 }, 1939); example.putScalar(new int[] { 0, 0, 2 }, 1955); example.putScalar(new int[] { 0, 1, 2 }, 2397); example.putScalar(new int[] { 0, 2, 2 }, 1953); example.putScalar(new int[] { 0, 0, 3 }, 1929); example.putScalar(new int[] { 0, 1, 3 }, 2386); example.putScalar(new int[] { 0, 2, 3 }, 1919); example.putScalar(new int[] { 0, 0, 4 }, 1895); example.putScalar(new int[] { 0, 1, 4 }, 2379); example.putScalar(new int[] { 0, 2, 4 }, 1918); example.putScalar(new int[] { 0, 0, 5 }, 1887); example.putScalar(new int[] { 0, 1, 5 }, 2389); example.putScalar(new int[] { 0, 2, 5 }, 1927); example.putScalar(new int[] { 0, 0, 6 }, 1895); example.putScalar(new int[] { 0, 1, 6 }, 2392); example.putScalar(new int[] { 0, 2, 6 }, 1929); example.putScalar(new int[] { 0, 0, 7 }, 1898); example.putScalar(new int[] { 0, 1, 7 }, 2402); example.putScalar(new int[] { 0, 2, 7 }, 1914); example.putScalar(new int[] { 0, 0, 8 }, 1882); example.putScalar(new int[] { 0, 1, 8 }, 2395); example.putScalar(new int[] { 0, 2, 8 }, 1894); example.putScalar(new int[] { 0, 0, 9 }, 1867); example.putScalar(new int[] { 0, 1, 9 }, 2403); example.putScalar(new int[] { 0, 2, 9 }, 1855); ds = new DataSet(example, null); normalizer.transform(ds); model.rnnClearPreviousState(); outputActivations = model.output(example); System.out.println("outputActivations expected 2 again: " + outputActivations); System.out.println("****************Example finished********************"); //return model; }