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

Example 76 with DataSet

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

the class FrozenLayerTest method cloneMLNFrozen.

@Test
public void cloneMLNFrozen() {
    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.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();
    INDArray asFrozenFeatures = modelToFineTune.feedForwardToLayer(2, randomData.getFeatures(), false).get(2);
    MultiLayerNetwork modelNow = new TransferLearning.Builder(modelToFineTune).setFeatureExtractor(1).build();
    MultiLayerNetwork clonedModel = modelNow.clone();
    //Check json
    assertEquals(clonedModel.getLayerWiseConfigurations().toJson(), modelNow.getLayerWiseConfigurations().toJson());
    //Check params
    assertEquals(modelNow.params(), clonedModel.params());
    MultiLayerNetwork notFrozen = new MultiLayerNetwork(overallConf.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(), Nd4j.hstack(modelToFineTune.getLayer(2).params(), modelToFineTune.getLayer(3).params()));
    int i = 0;
    while (i < 5) {
        notFrozen.fit(new DataSet(asFrozenFeatures, randomData.getLabels()));
        modelNow.fit(randomData);
        clonedModel.fit(randomData);
        i++;
    }
    INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer(0).params(), modelToFineTune.getLayer(1).params(), notFrozen.params());
    assertEquals(expectedParams, modelNow.params());
    assertEquals(expectedParams, clonedModel.params());
}

Example 77 with DataSet

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

the class FrozenLayerTest method testFrozen.

/*
        A model with a few frozen layers ==
            Model with non frozen layers set with the output of the forward pass of the frozen layers
     */
@Test
public void testFrozen() {
    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);
    FineTuneConfiguration finetune = new FineTuneConfiguration.Builder().learningRate(0.1).build();
    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();
    List<INDArray> ff = modelToFineTune.feedForwardToLayer(2, randomData.getFeatures(), false);
    INDArray asFrozenFeatures = ff.get(2);
    MultiLayerNetwork modelNow = new TransferLearning.Builder(modelToFineTune).fineTuneConfiguration(finetune).setFeatureExtractor(1).build();
    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(modelNow.getLayer(2).conf(), notFrozen.getLayer(0).conf());  //Equal, other than names
    //        assertEquals(modelNow.getLayer(3).conf(), notFrozen.getLayer(1).conf());  //Equal, other than names
    //Check: forward pass
    INDArray outNow = modelNow.output(randomData.getFeatures());
    INDArray outNotFrozen = notFrozen.output(asFrozenFeatures);
    assertEquals(outNow, outNotFrozen);
    for (int i = 0; i < 5; i++) {
        notFrozen.fit(new DataSet(asFrozenFeatures, randomData.getLabels()));
        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 78 with DataSet

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

the class FrozenLayerTest method cloneCompGraphFrozen.

@Test
public void cloneCompGraphFrozen() {
    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);
    ComputationGraph modelToFineTune = new ComputationGraph(overallConf.graphBuilder().addInputs("layer0In").addLayer("layer0", new DenseLayer.Builder().nIn(4).nOut(3).build(), "layer0In").addLayer("layer1", new DenseLayer.Builder().nIn(3).nOut(2).build(), "layer0").addLayer("layer2", new DenseLayer.Builder().nIn(2).nOut(3).build(), "layer1").addLayer("layer3", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build(), "layer2").setOutputs("layer3").build());
    modelToFineTune.init();
    INDArray asFrozenFeatures = modelToFineTune.feedForward(randomData.getFeatures(), false).get("layer1");
    ComputationGraph modelNow = new TransferLearning.GraphBuilder(modelToFineTune).setFeatureExtractor("layer1").build();
    ComputationGraph clonedModel = modelNow.clone();
    //Check json
    assertEquals(clonedModel.getConfiguration().toJson(), modelNow.getConfiguration().toJson());
    //Check params
    assertEquals(modelNow.params(), clonedModel.params());
    ComputationGraph notFrozen = new ComputationGraph(overallConf.graphBuilder().addInputs("layer0In").addLayer("layer0", new DenseLayer.Builder().nIn(2).nOut(3).build(), "layer0In").addLayer("layer1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build(), "layer0").setOutputs("layer1").build());
    notFrozen.init();
    notFrozen.setParams(Nd4j.hstack(modelToFineTune.getLayer("layer2").params(), modelToFineTune.getLayer("layer3").params()));
    int i = 0;
    while (i < 5) {
        notFrozen.fit(new DataSet(asFrozenFeatures, randomData.getLabels()));
        modelNow.fit(randomData);
        clonedModel.fit(randomData);
        i++;
    }
    INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer("layer0").params(), modelToFineTune.getLayer("layer1").params(), notFrozen.params());
    assertEquals(expectedParams, modelNow.params());
    assertEquals(expectedParams, clonedModel.params());
}

Example 79 with DataSet

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

the class OutputLayerTest method test3.

@Test
public void test3() {
    org.nd4j.linalg.dataset.api.iterator.DataSetIterator iter = new IrisDataSetIterator(150, 150);
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().iterations(3).miniBatch(false).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(4).nOut(3).activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    org.deeplearning4j.nn.layers.OutputLayer layer = (org.deeplearning4j.nn.layers.OutputLayer) conf.getLayer().instantiate(conf, null, 0, params, true);
    layer.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
    DataSet next = iter.next();
    next.normalizeZeroMeanZeroUnitVariance();
    layer.setListeners(new ScoreIterationListener(1));
    layer.fit(next);
}

Example 80 with DataSet

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

the class GravesBidirectionalLSTMTest method testConvergence.

@Test
@Ignore
public void testConvergence() {
    Nd4j.getRandom().setSeed(12345);
    final int state1Len = 100;
    final int state2Len = 30;
    //segment by signal mean
    //Data: has shape [miniBatchSize,nIn,timeSeriesLength];
    final INDArray sig1 = Nd4j.randn(new int[] { 1, 2, state1Len }).mul(0.1);
    final INDArray sig2 = Nd4j.randn(new int[] { 1, 2, state2Len }).mul(0.1).add(Nd4j.ones(new int[] { 1, 2, state2Len }).mul(1.0));
    INDArray sig = Nd4j.concat(2, sig1, sig2);
    INDArray labels = Nd4j.zeros(new int[] { 1, 2, state1Len + state2Len });
    for (int t = 0; t < state1Len; t++) {
        labels.putScalar(new int[] { 0, 0, t }, 1.0);
    }
    for (int t = state1Len; t < state1Len + state2Len; t++) {
        labels.putScalar(new int[] { 0, 1, t }, 1.0);
    }
    for (int i = 0; i < 3; i++) {
        sig = Nd4j.concat(2, sig, sig);
        labels = Nd4j.concat(2, labels, labels);
    }
    final DataSet ds = new DataSet(sig, labels);
    final MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(5).learningRate(0.1).rmsDecay(0.95).regularization(true).l2(0.001).updater(Updater.ADAGRAD).seed(12345).list().pretrain(false).layer(0, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(1, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(2, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(2).activation(Activation.TANH).build()).backprop(true).build();
    final MultiLayerNetwork net = new MultiLayerNetwork(conf);
    final IterationListener scoreSaver = new IterationListener() {

        @Override
        public boolean invoked() {
            return false;
        }

        @Override
        public void invoke() {
        }

        @Override
        public void iterationDone(Model model, int iteration) {
            score = model.score();
        }
    };
    net.setListeners(scoreSaver, new ScoreIterationListener(1));
    double oldScore = Double.POSITIVE_INFINITY;
    net.init();
    for (int iEpoch = 0; iEpoch < 3; iEpoch++) {
        net.fit(ds);
        System.out.print(String.format("score is %f%n", score));
        assertTrue(!Double.isNaN(score));
        assertTrue(score < 0.9 * oldScore);
        oldScore = score;
        final INDArray output = net.output(ds.getFeatureMatrix());
        Evaluation evaluation = new Evaluation();
        evaluation.evalTimeSeries(ds.getLabels(), output);
        System.out.print(evaluation.stats() + "\n");
    }
}