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

Example 96 with DataSet

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

the class TestRenders method renderHistogram.

@Test
public void renderHistogram() throws Exception {
    MnistDataFetcher fetcher = new MnistDataFetcher(true);
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().momentum(0.9f).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(100).learningRate(1e-1f).layer(new org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder().nIn(784).nOut(600).corruptionLevel(0.6).weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build()).build();
    fetcher.fetch(100);
    DataSet d2 = fetcher.next();
    INDArray input = d2.getFeatureMatrix();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    AutoEncoder da = (AutoEncoder) conf.getLayer().instantiate(conf, null, 0, params, true);
    da.setListeners(new ScoreIterationListener(1), new HistogramIterationListener(5));
    da.setParams(da.params());
    da.fit(input);
}

Example 97 with DataSet

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

the class TestRenders method renderHistogram2.

@Test
public void renderHistogram2() throws Exception {
    MnistDataFetcher fetcher = new MnistDataFetcher(true);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().momentum(0.9f).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1000).learningRate(1e-1f).list().layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(784).nOut(100).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(100).nOut(10).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    net.setListeners(Arrays.<IterationListener>asList(new ScoreIterationListener(1), new HistogramIterationListener(1, true)));
    fetcher.fetch(100);
    DataSet d2 = fetcher.next();
    net.fit(d2);
}

Example 98 with DataSet

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

the class TransferLearningHelperTest method testMLN.

@Test
public void testMLN() {
    DataSet randomData = new DataSet(Nd4j.rand(10, 4), Nd4j.rand(10, 3));
    NeuralNetConfiguration.Builder overallConf = new NeuralNetConfiguration.Builder().learningRate(0.1).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD).activation(Activation.IDENTITY);
    MultiLayerNetwork modelToFineTune = new MultiLayerNetwork(overallConf.clone().list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build()).layer(1, new DenseLayer.Builder().nIn(3).nOut(2).build()).layer(2, new DenseLayer.Builder().nIn(2).nOut(3).build()).layer(3, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).build());
    modelToFineTune.init();
    MultiLayerNetwork modelNow = new TransferLearning.Builder(modelToFineTune).setFeatureExtractor(1).build();
    List<INDArray> ff = modelToFineTune.feedForwardToLayer(2, randomData.getFeatures(), false);
    INDArray asFrozenFeatures = ff.get(2);
    TransferLearningHelper helper = new TransferLearningHelper(modelToFineTune, 1);
    INDArray paramsLastTwoLayers = Nd4j.hstack(modelToFineTune.getLayer(2).params(), modelToFineTune.getLayer(3).params());
    MultiLayerNetwork notFrozen = new MultiLayerNetwork(overallConf.clone().list().layer(0, new DenseLayer.Builder().nIn(2).nOut(3).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).build(), paramsLastTwoLayers);
    assertEquals(asFrozenFeatures, helper.featurize(randomData).getFeatures());
    assertEquals(randomData.getLabels(), helper.featurize(randomData).getLabels());
    for (int i = 0; i < 5; i++) {
        notFrozen.fit(new DataSet(asFrozenFeatures, randomData.getLabels()));
        helper.fitFeaturized(helper.featurize(randomData));
        modelNow.fit(randomData);
    }
    INDArray expected = Nd4j.hstack(modelToFineTune.getLayer(0).params(), modelToFineTune.getLayer(1).params(), notFrozen.params());
    INDArray act = modelNow.params();
    assertEquals(expected, act);
}

Example 99 with DataSet

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

the class TransferLearningMLNTest method testRemoveAndAdd.

@Test
public void testRemoveAndAdd() {
    DataSet randomData = new DataSet(Nd4j.rand(10, 4), Nd4j.rand(10, 3));
    NeuralNetConfiguration.Builder equivalentConf = new NeuralNetConfiguration.Builder().learningRate(0.1).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD);
    FineTuneConfiguration overallConf = new FineTuneConfiguration.Builder().learningRate(0.1).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD).build();
    MultiLayerNetwork modelToFineTune = new //overallConf.list()
    MultiLayerNetwork(equivalentConf.list().layer(0, new DenseLayer.Builder().nIn(4).nOut(5).build()).layer(1, new DenseLayer.Builder().nIn(5).nOut(2).build()).layer(2, new DenseLayer.Builder().nIn(2).nOut(3).build()).layer(3, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).build());
    modelToFineTune.init();
    MultiLayerNetwork modelNow = new TransferLearning.Builder(modelToFineTune).fineTuneConfiguration(overallConf).nOutReplace(0, 7, WeightInit.XAVIER, WeightInit.XAVIER).nOutReplace(2, 5, WeightInit.XAVIER).removeOutputLayer().addLayer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(3).learningRate(0.5).activation(Activation.SOFTMAX).build()).build();
    MultiLayerNetwork modelExpectedArch = new MultiLayerNetwork(equivalentConf.list().layer(0, new DenseLayer.Builder().nIn(4).nOut(7).build()).layer(1, new DenseLayer.Builder().nIn(7).nOut(2).build()).layer(2, new DenseLayer.Builder().nIn(2).nOut(5).build()).layer(3, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).learningRate(0.5).nIn(5).nOut(3).build()).build());
    modelExpectedArch.init();
    //modelNow should have the same architecture as modelExpectedArch
    assertArrayEquals(modelExpectedArch.params().shape(), modelNow.params().shape());
    assertArrayEquals(modelExpectedArch.getLayer(0).params().shape(), modelNow.getLayer(0).params().shape());
    assertArrayEquals(modelExpectedArch.getLayer(1).params().shape(), modelNow.getLayer(1).params().shape());
    assertArrayEquals(modelExpectedArch.getLayer(2).params().shape(), modelNow.getLayer(2).params().shape());
    assertArrayEquals(modelExpectedArch.getLayer(3).params().shape(), modelNow.getLayer(3).params().shape());
    modelNow.setParams(modelExpectedArch.params());
    //fit should give the same results
    modelExpectedArch.fit(randomData);
    modelNow.fit(randomData);
    assertTrue(modelExpectedArch.score() == modelNow.score());
    assertEquals(modelExpectedArch.params(), modelNow.params());
}

Example 100 with DataSet

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

the class TransferLearningMLNTest method simpleFineTune.

@Test
public void simpleFineTune() {
    long rng = 12345L;
    DataSet randomData = new DataSet(Nd4j.rand(10, 4), Nd4j.rand(10, 3));
    //original conf
    NeuralNetConfiguration.Builder confToChange = new NeuralNetConfiguration.Builder().seed(rng).optimizationAlgo(OptimizationAlgorithm.LBFGS).updater(Updater.NESTEROVS).momentum(0.99).learningRate(0.01);
    MultiLayerNetwork modelToFineTune = new MultiLayerNetwork(confToChange.list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).build());
    modelToFineTune.init();
    //model after applying changes with transfer learning
    MultiLayerNetwork modelNow = new TransferLearning.Builder(modelToFineTune).fineTuneConfiguration(new FineTuneConfiguration.Builder().seed(rng).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.RMSPROP).learningRate(//Intent: override both weight and bias LR, unless bias LR is manually set also
    0.5).l2(0.4).regularization(true).build()).build();
    for (org.deeplearning4j.nn.api.Layer l : modelNow.getLayers()) {
        assertEquals(Updater.RMSPROP, l.conf().getLayer().getUpdater());
        assertEquals(0.5, l.conf().getLayer().getLearningRate(), 1e-6);
    }
    NeuralNetConfiguration.Builder confSet = new NeuralNetConfiguration.Builder().seed(rng).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.RMSPROP).learningRate(0.5).l2(0.4).regularization(true);
    MultiLayerNetwork expectedModel = new MultiLayerNetwork(confSet.list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).build());
    expectedModel.init();
    expectedModel.setParams(modelToFineTune.params().dup());
    assertEquals(expectedModel.params(), modelNow.params());
    //Check json
    MultiLayerConfiguration expectedConf = expectedModel.getLayerWiseConfigurations();
    assertEquals(expectedConf.toJson(), modelNow.getLayerWiseConfigurations().toJson());
    //Check params after fit
    modelNow.fit(randomData);
    expectedModel.fit(randomData);
    assertEquals(modelNow.score(), expectedModel.score(), 1e-6);
    INDArray pExp = expectedModel.params();
    INDArray pNow = modelNow.params();
    assertEquals(pExp, pNow);
}