Examples with DataSet org.nd4j.linalg.dataset.DataSet used on opensource projects

Example 91 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class DenseTest method testMLPMultiLayerPretrain.

@Test
public void testMLPMultiLayerPretrain() {
    // Note CNN does not do pretrain
    MultiLayerNetwork model = getDenseMLNConfig(false, true);
    model.fit(iter);
    MultiLayerNetwork model2 = getDenseMLNConfig(false, true);
    model2.fit(iter);
    iter.reset();
    DataSet test = iter.next();
    assertEquals(model.params(), model2.params());
    Evaluation eval = new Evaluation();
    INDArray output = model.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    double f1Score = eval.f1();
    Evaluation eval2 = new Evaluation();
    INDArray output2 = model2.output(test.getFeatureMatrix());
    eval2.eval(test.getLabels(), output2);
    double f1Score2 = eval2.f1();
    assertEquals(f1Score, f1Score2, 1e-4);
}

Example 92 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerSetupTest method testCNNDBNMultiLayer.

@Test
public void testCNNDBNMultiLayer() throws Exception {
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();
    // Run with separate activation layer
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(2).seed(123).weightInit(WeightInit.XAVIER).list().layer(0, new ConvolutionLayer.Builder(new int[] { 1, 1 }, new int[] { 1, 1 }).nIn(1).nOut(6).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().build()).layer(2, new ActivationLayer.Builder().activation(Activation.RELU).build()).layer(3, new DenseLayer.Builder().nIn(28 * 28 * 6).nOut(10).activation(Activation.IDENTITY).build()).layer(4, new BatchNormalization.Builder().nOut(10).build()).layer(5, new ActivationLayer.Builder().activation(Activation.RELU).build()).layer(6, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nOut(10).build()).backprop(true).pretrain(false).cnnInputSize(28, 28, 1).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.setInput(next.getFeatureMatrix());
    INDArray activationsActual = network.preOutput(next.getFeatureMatrix());
    assertEquals(10, activationsActual.shape()[1], 1e-2);
    network.fit(next);
    INDArray actualGammaParam = network.getLayer(1).getParam(BatchNormalizationParamInitializer.GAMMA);
    INDArray actualBetaParam = network.getLayer(1).getParam(BatchNormalizationParamInitializer.BETA);
    assertTrue(actualGammaParam != null);
    assertTrue(actualBetaParam != null);
}

Example 93 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class Word2VecDataFetcher method next.

@Override
public DataSet next() {
    //pop from cache when possible, or when there's nothing left
    if (cache.size() >= batch || !files.hasNext())
        return fromCache();
    File f = files.next();
    try {
        LineIterator lines = FileUtils.lineIterator(f);
        INDArray outcomes = null;
        INDArray input = null;
        while (lines.hasNext()) {
            List<Window> windows = Windows.windows(lines.nextLine());
            if (windows.isEmpty() && lines.hasNext())
                continue;
            if (windows.size() < batch) {
                input = Nd4j.create(windows.size(), vec.lookupTable().layerSize() * vec.getWindow());
                outcomes = Nd4j.create(batch, labels.size());
                for (int i = 0; i < windows.size(); i++) {
                    input.putRow(i, WindowConverter.asExampleMatrix(cache.get(i), vec));
                    int idx = labels.indexOf(windows.get(i).getLabel());
                    if (idx < 0)
                        idx = 0;
                    INDArray outcomeRow = FeatureUtil.toOutcomeVector(idx, labels.size());
                    outcomes.putRow(i, outcomeRow);
                }
                return new DataSet(input, outcomes);
            } else {
                input = Nd4j.create(batch, vec.lookupTable().layerSize() * vec.getWindow());
                outcomes = Nd4j.create(batch, labels.size());
                for (int i = 0; i < batch; i++) {
                    input.putRow(i, WindowConverter.asExampleMatrix(cache.get(i), vec));
                    int idx = labels.indexOf(windows.get(i).getLabel());
                    if (idx < 0)
                        idx = 0;
                    INDArray outcomeRow = FeatureUtil.toOutcomeVector(idx, labels.size());
                    outcomes.putRow(i, outcomeRow);
                }
                /*
                     * Note that I'm aware of possible concerns for sentence sequencing.
                     * This is a hack right now in place of something
                     * that will be way more elegant in the future.
                     */
                if (windows.size() > batch) {
                    List<Window> leftOvers = windows.subList(batch, windows.size());
                    cache.addAll(leftOvers);
                }
                return new DataSet(input, outcomes);
            }
        }
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
    return null;
}

Example 94 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class ManualTests method testFlowActivationsMLN1.

@Test
public void testFlowActivationsMLN1() throws Exception {
    int nChannels = 1;
    int outputNum = 10;
    int batchSize = 64;
    int nEpochs = 10;
    int iterations = 1;
    int seed = 123;
    log.info("Load data....");
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
    DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, 12345);
    log.info("Build model....");
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(iterations).regularization(true).l2(0.0005).learningRate(//.biasLearningRate(0.02)
    0.01).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(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(Activation.IDENTITY).build()).layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2).build()).layer(4, new DenseLayer.Builder().activation(Activation.RELU).nOut(500).build()).layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum).activation(Activation.SOFTMAX).build()).backprop(true).pretrain(false);
    // The builder needs the dimensions of the image along with the number of channels. these are 28x28 images in one channel
    new ConvolutionLayerSetup(builder, 28, 28, 1);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    log.info("Train model....");
    model.setListeners(new FlowIterationListener(1));
    for (int i = 0; i < nEpochs; i++) {
        model.fit(mnistTrain);
        log.info("*** Completed epoch {} ***", i);
        mnistTest.reset();
    }
    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(outputNum);
    while (mnistTest.hasNext()) {
        DataSet ds = mnistTest.next();
        INDArray output = model.output(ds.getFeatureMatrix(), false);
        eval.eval(ds.getLabels(), output);
    }
    log.info(eval.stats());
    log.info("****************Example finished********************");
}

Example 95 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class ManualTests method testCNNActivations2.

@Test
public void testCNNActivations2() throws Exception {
    int nChannels = 1;
    int outputNum = 10;
    int batchSize = 64;
    int nEpochs = 10;
    int iterations = 1;
    int seed = 123;
    log.info("Load data....");
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
    DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, 12345);
    log.info("Build model....");
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(iterations).regularization(true).l2(0.0005).learningRate(//.biasLearningRate(0.02)
    0.01).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(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(Activation.IDENTITY).build()).layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2).build()).layer(4, new DenseLayer.Builder().activation(Activation.RELU).nOut(500).build()).layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum).activation(Activation.SOFTMAX).build()).backprop(true).pretrain(false);
    // The builder needs the dimensions of the image along with the number of channels. these are 28x28 images in one channel
    new ConvolutionLayerSetup(builder, 28, 28, 1);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    /*
        ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
            .averagingFrequency(1)
            .prefetchBuffer(12)
            .workers(2)
            .reportScoreAfterAveraging(false)
            .useLegacyAveraging(false)
            .build();
        */
    log.info("Train model....");
    model.setListeners(new ConvolutionalIterationListener(1));
    //((NativeOpExecutioner) Nd4j.getExecutioner()).getLoop().setOmpNumThreads(8);
    long timeX = System.currentTimeMillis();
    //        nEpochs = 2;
    for (int i = 0; i < nEpochs; i++) {
        long time1 = System.currentTimeMillis();
        model.fit(mnistTrain);
        //wrapper.fit(mnistTrain);
        long time2 = System.currentTimeMillis();
        log.info("*** Completed epoch {}, Time elapsed: {} ***", i, (time2 - time1));
    }
    long timeY = System.currentTimeMillis();
    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(outputNum);
    while (mnistTest.hasNext()) {
        DataSet ds = mnistTest.next();
        INDArray output = model.output(ds.getFeatureMatrix(), false);
        eval.eval(ds.getLabels(), output);
    }
    log.info(eval.stats());
    mnistTest.reset();
    log.info("****************Example finished********************");
}