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

Example 71 with DataSet

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

the class SamplingDataSetIterator method next.

@Override
public DataSet next() {
    DataSet ret = sampleFrom.sample(batchSize);
    numTimesSampled += batchSize;
    return ret;
}

Example 72 with DataSet

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

the class DataSetLossCalculator method calculateScore.

@Override
public double calculateScore(MultiLayerNetwork network) {
    dataSetIterator.reset();
    double lossSum = 0.0;
    int exCount = 0;
    while (dataSetIterator.hasNext()) {
        DataSet dataSet = dataSetIterator.next();
        if (dataSet == null)
            break;
        int nEx = dataSet.getFeatureMatrix().size(0);
        lossSum += network.score(dataSet) * nEx;
        exCount += nEx;
    }
    if (average)
        return lossSum / exCount;
    else
        return lossSum;
}

Example 73 with DataSet

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

the class BaseDatasetIterator method next.

@Override
public DataSet next(int num) {
    fetcher.fetch(num);
    DataSet next = fetcher.next();
    if (preProcessor != null)
        preProcessor.preProcess(next);
    return next;
}

Example 74 with DataSet

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

the class ExistingDataSetIterator method next.

@Override
public DataSet next() {
    if (preProcessor != null) {
        DataSet ds = iterator.next();
        if (!ds.isPreProcessed()) {
            preProcessor.preProcess(ds);
            ds.markAsPreProcessed();
        }
        return ds;
    } else
        return iterator.next();
}

Example 75 with DataSet

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

the class DropoutLayerTest method testDropoutLayerWithConvMnist.

@Test
public void testDropoutLayerWithConvMnist() throws Exception {
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();
    // Run without separate activation layer
    MultiLayerConfiguration confIntegrated = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new ConvolutionLayer.Builder(4, 4).stride(2, 2).nIn(1).nOut(20).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).dropOut(0.25).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
    MultiLayerNetwork netIntegrated = new MultiLayerNetwork(confIntegrated);
    netIntegrated.init();
    netIntegrated.fit(next);
    // Run with separate activation layer
    MultiLayerConfiguration confSeparate = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new ConvolutionLayer.Builder(4, 4).stride(2, 2).nIn(1).nOut(20).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new DropoutLayer.Builder(0.25).build()).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
    MultiLayerNetwork netSeparate = new MultiLayerNetwork(confSeparate);
    netSeparate.init();
    netSeparate.fit(next);
    // check parameters
    assertEquals(netIntegrated.getLayer(0).getParam("W"), netSeparate.getLayer(0).getParam("W"));
    assertEquals(netIntegrated.getLayer(0).getParam("b"), netSeparate.getLayer(0).getParam("b"));
    assertEquals(netIntegrated.getLayer(1).getParam("W"), netSeparate.getLayer(2).getParam("W"));
    assertEquals(netIntegrated.getLayer(1).getParam("b"), netSeparate.getLayer(2).getParam("b"));
    // check activations
    netIntegrated.setInput(next.getFeatureMatrix());
    netSeparate.setInput(next.getFeatureMatrix());
    Nd4j.getRandom().setSeed(12345);
    List<INDArray> actTrainIntegrated = netIntegrated.feedForward(true);
    Nd4j.getRandom().setSeed(12345);
    List<INDArray> actTrainSeparate = netSeparate.feedForward(true);
    assertEquals(actTrainIntegrated.get(1), actTrainSeparate.get(1));
    assertEquals(actTrainIntegrated.get(2), actTrainSeparate.get(3));
    Nd4j.getRandom().setSeed(12345);
    List<INDArray> actTestIntegrated = netIntegrated.feedForward(false);
    Nd4j.getRandom().setSeed(12345);
    List<INDArray> actTestSeparate = netSeparate.feedForward(false);
    assertEquals(actTestIntegrated.get(1), actTrainSeparate.get(1));
    assertEquals(actTestIntegrated.get(2), actTestSeparate.get(3));
}