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

Example 1 with MultiDataSet

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

the class ComputationGraphTestRNN method checkMaskArrayClearance.

@Test
public void checkMaskArrayClearance() {
    for (boolean tbptt : new boolean[] { true, false }) {
        //Simple "does it throw an exception" type test...
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().iterations(1).seed(12345).graphBuilder().addInputs("in").addLayer("out", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nIn(1).nOut(1).build(), "in").setOutputs("out").backpropType(tbptt ? BackpropType.TruncatedBPTT : BackpropType.Standard).tBPTTForwardLength(8).tBPTTBackwardLength(8).build();
        ComputationGraph net = new ComputationGraph(conf);
        net.init();
        MultiDataSet data = new MultiDataSet(new INDArray[] { Nd4j.linspace(1, 10, 10).reshape(1, 1, 10) }, new INDArray[] { Nd4j.linspace(2, 20, 10).reshape(1, 1, 10) }, new INDArray[] { Nd4j.ones(10) }, new INDArray[] { Nd4j.ones(10) });
        net.fit(data);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        DataSet ds = new DataSet(data.getFeatures(0), data.getLabels(0), data.getFeaturesMaskArray(0), data.getLabelsMaskArray(0));
        net.fit(ds);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        net.fit(data.getFeatures(), data.getLabels(), data.getFeaturesMaskArrays(), data.getLabelsMaskArrays());
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        MultiDataSetIterator iter = new IteratorMultiDataSetIterator(Collections.singletonList((org.nd4j.linalg.dataset.api.MultiDataSet) data).iterator(), 1);
        net.fit(iter);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        DataSetIterator iter2 = new IteratorDataSetIterator(Collections.singletonList(ds).iterator(), 1);
        net.fit(iter2);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
    }
}

Example 2 with MultiDataSet

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

the class TransferLearningHelperTest method testFitUnFrozen.

@Test
public void testFitUnFrozen() {
    NeuralNetConfiguration.Builder overallConf = new NeuralNetConfiguration.Builder().learningRate(0.9).seed(124).activation(Activation.IDENTITY).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD);
    ComputationGraphConfiguration conf = overallConf.graphBuilder().addInputs("inCentre", "inRight").addLayer("denseCentre0", new DenseLayer.Builder().nIn(10).nOut(9).build(), "inCentre").addLayer("denseCentre1", new DenseLayer.Builder().nIn(9).nOut(8).build(), "denseCentre0").addLayer("denseCentre2", new DenseLayer.Builder().nIn(8).nOut(7).build(), "denseCentre1").addLayer("denseCentre3", new DenseLayer.Builder().nIn(7).nOut(7).build(), "denseCentre2").addLayer("outCentre", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(7).nOut(4).build(), "denseCentre3").addVertex("subsetLeft", new SubsetVertex(0, 3), "denseCentre1").addLayer("denseLeft0", new DenseLayer.Builder().nIn(4).nOut(5).build(), "subsetLeft").addLayer("outLeft", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(5).nOut(6).build(), "denseLeft0").addLayer("denseRight", new DenseLayer.Builder().nIn(7).nOut(7).build(), "denseCentre2").addLayer("denseRight0", new DenseLayer.Builder().nIn(2).nOut(3).build(), "inRight").addVertex("mergeRight", new MergeVertex(), "denseRight", "denseRight0").addLayer("denseRight1", new DenseLayer.Builder().nIn(10).nOut(5).build(), "mergeRight").addLayer("outRight", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(5).nOut(5).build(), "denseRight1").setOutputs("outLeft", "outCentre", "outRight").build();
    ComputationGraph modelToTune = new ComputationGraph(conf);
    modelToTune.init();
    INDArray inRight = Nd4j.rand(10, 2);
    INDArray inCentre = Nd4j.rand(10, 10);
    INDArray outLeft = Nd4j.rand(10, 6);
    INDArray outRight = Nd4j.rand(10, 5);
    INDArray outCentre = Nd4j.rand(10, 4);
    MultiDataSet origData = new MultiDataSet(new INDArray[] { inCentre, inRight }, new INDArray[] { outLeft, outCentre, outRight });
    ComputationGraph modelIdentical = modelToTune.clone();
    modelIdentical.getVertex("denseCentre0").setLayerAsFrozen();
    modelIdentical.getVertex("denseCentre1").setLayerAsFrozen();
    modelIdentical.getVertex("denseCentre2").setLayerAsFrozen();
    TransferLearningHelper helper = new TransferLearningHelper(modelToTune, "denseCentre2");
    MultiDataSet featurizedDataSet = helper.featurize(origData);
    assertEquals(modelIdentical.getLayer("denseRight0").params(), modelToTune.getLayer("denseRight0").params());
    modelIdentical.fit(origData);
    helper.fitFeaturized(featurizedDataSet);
    assertEquals(modelIdentical.getLayer("denseCentre0").params(), modelToTune.getLayer("denseCentre0").params());
    assertEquals(modelIdentical.getLayer("denseCentre1").params(), modelToTune.getLayer("denseCentre1").params());
    assertEquals(modelIdentical.getLayer("denseCentre2").params(), modelToTune.getLayer("denseCentre2").params());
    assertEquals(modelIdentical.getLayer("denseCentre3").params(), modelToTune.getLayer("denseCentre3").params());
    assertEquals(modelIdentical.getLayer("outCentre").params(), modelToTune.getLayer("outCentre").params());
    assertEquals(modelIdentical.getLayer("denseRight").conf().toJson(), modelToTune.getLayer("denseRight").conf().toJson());
    assertEquals(modelIdentical.getLayer("denseRight").params(), modelToTune.getLayer("denseRight").params());
    assertEquals(modelIdentical.getLayer("denseRight0").conf().toJson(), modelToTune.getLayer("denseRight0").conf().toJson());
    //assertEquals(modelIdentical.getLayer("denseRight0").params(),modelToTune.getLayer("denseRight0").params());
    assertEquals(modelIdentical.getLayer("denseRight1").params(), modelToTune.getLayer("denseRight1").params());
    assertEquals(modelIdentical.getLayer("outRight").params(), modelToTune.getLayer("outRight").params());
    assertEquals(modelIdentical.getLayer("denseLeft0").params(), modelToTune.getLayer("denseLeft0").params());
    assertEquals(modelIdentical.getLayer("outLeft").params(), modelToTune.getLayer("outLeft").params());
    log.info(modelIdentical.summary());
    log.info(helper.unfrozenGraph().summary());
}

Example 3 with MultiDataSet

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

the class TransferLearningComplex method testSimplerMergeBackProp.

@Test
public void testSimplerMergeBackProp() {
    NeuralNetConfiguration.Builder overallConf = new NeuralNetConfiguration.Builder().learningRate(0.9).activation(Activation.IDENTITY).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD);
    /*
                inCentre                inRight
                   |                        |
             denseCentre0               denseRight0
                   |                        |
                   |------ mergeRight ------|
                                |
                              outRight
        
        */
    ComputationGraphConfiguration conf = overallConf.graphBuilder().addInputs("inCentre", "inRight").addLayer("denseCentre0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "inCentre").addLayer("denseRight0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "inRight").addVertex("mergeRight", new MergeVertex(), "denseCentre0", "denseRight0").addLayer("outRight", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(4).nOut(2).build(), "mergeRight").setOutputs("outRight").build();
    ComputationGraph modelToTune = new ComputationGraph(conf);
    modelToTune.init();
    MultiDataSet randData = new MultiDataSet(new INDArray[] { Nd4j.rand(2, 2), Nd4j.rand(2, 2) }, new INDArray[] { Nd4j.rand(2, 2) });
    INDArray denseCentre0 = modelToTune.feedForward(randData.getFeatures(), false).get("denseCentre0");
    MultiDataSet otherRandData = new MultiDataSet(new INDArray[] { denseCentre0, randData.getFeatures(1) }, randData.getLabels());
    ComputationGraphConfiguration otherConf = overallConf.graphBuilder().addInputs("denseCentre0", "inRight").addLayer("denseRight0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "inRight").addVertex("mergeRight", new MergeVertex(), "denseCentre0", "denseRight0").addLayer("outRight", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(4).nOut(2).build(), "mergeRight").setOutputs("outRight").build();
    ComputationGraph modelOther = new ComputationGraph(otherConf);
    modelOther.init();
    modelOther.getLayer("denseRight0").setParams(modelToTune.getLayer("denseRight0").params());
    modelOther.getLayer("outRight").setParams(modelToTune.getLayer("outRight").params());
    modelToTune.getVertex("denseCentre0").setLayerAsFrozen();
    ComputationGraph modelNow = new TransferLearning.GraphBuilder(modelToTune).setFeatureExtractor("denseCentre0").build();
    int n = 0;
    while (n < 5) {
        if (n == 0) {
            //confirm activations out of the merge are equivalent
            assertEquals(modelToTune.feedForward(randData.getFeatures(), false).get("mergeRight"), modelOther.feedForward(otherRandData.getFeatures(), false).get("mergeRight"));
            assertEquals(modelNow.feedForward(randData.getFeatures(), false).get("mergeRight"), modelOther.feedForward(otherRandData.getFeatures(), false).get("mergeRight"));
        }
        //confirm activations out of frozen vertex is the same as the input to the other model
        modelOther.fit(otherRandData);
        modelToTune.fit(randData);
        modelNow.fit(randData);
        assertEquals(otherRandData.getFeatures(0), modelNow.feedForward(randData.getFeatures(), false).get("denseCentre0"));
        assertEquals(otherRandData.getFeatures(0), modelToTune.feedForward(randData.getFeatures(), false).get("denseCentre0"));
        assertEquals(modelOther.getLayer("denseRight0").params(), modelNow.getLayer("denseRight0").params());
        assertEquals(modelOther.getLayer("denseRight0").params(), modelToTune.getLayer("denseRight0").params());
        assertEquals(modelOther.getLayer("outRight").params(), modelNow.getLayer("outRight").params());
        assertEquals(modelOther.getLayer("outRight").params(), modelToTune.getLayer("outRight").params());
        n++;
    }
}

Example 4 with MultiDataSet

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

the class TransferLearningComplex method testLessSimpleMergeBackProp.

@Test
public void testLessSimpleMergeBackProp() {
    NeuralNetConfiguration.Builder overallConf = new NeuralNetConfiguration.Builder().learningRate(0.9).activation(Activation.IDENTITY).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD);
    /*
                inCentre                inRight
                   |                        |
             denseCentre0               denseRight0
                   |                        |
                   |------ mergeRight ------|
                   |            |
                 outCentre     outRight
        
        */
    ComputationGraphConfiguration conf = overallConf.graphBuilder().addInputs("inCentre", "inRight").addLayer("denseCentre0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "inCentre").addLayer("outCentre", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(2).nOut(2).build(), "denseCentre0").addLayer("denseRight0", new DenseLayer.Builder().nIn(3).nOut(2).build(), "inRight").addVertex("mergeRight", new MergeVertex(), "denseCentre0", "denseRight0").addLayer("outRight", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(4).nOut(2).build(), "mergeRight").setOutputs("outRight").setOutputs("outCentre").build();
    ComputationGraph modelToTune = new ComputationGraph(conf);
    modelToTune.init();
    modelToTune.getVertex("denseCentre0").setLayerAsFrozen();
    MultiDataSet randData = new MultiDataSet(new INDArray[] { Nd4j.rand(2, 2), Nd4j.rand(2, 3) }, new INDArray[] { Nd4j.rand(2, 2), Nd4j.rand(2, 2) });
    INDArray denseCentre0 = modelToTune.feedForward(randData.getFeatures(), false).get("denseCentre0");
    MultiDataSet otherRandData = new MultiDataSet(new INDArray[] { denseCentre0, randData.getFeatures(1) }, randData.getLabels());
    ComputationGraph modelNow = new TransferLearning.GraphBuilder(modelToTune).setFeatureExtractor("denseCentre0").build();
    assertTrue(modelNow.getLayer("denseCentre0") instanceof FrozenLayer);
    int n = 0;
    while (n < 5) {
        if (n == 0) {
            //confirm activations out of the merge are equivalent
            assertEquals(modelToTune.feedForward(randData.getFeatures(), false).get("mergeRight"), modelNow.feedForward(otherRandData.getFeatures(), false).get("mergeRight"));
        }
        //confirm activations out of frozen vertex is the same as the input to the other model
        modelToTune.fit(randData);
        modelNow.fit(randData);
        assertEquals(otherRandData.getFeatures(0), modelNow.feedForward(randData.getFeatures(), false).get("denseCentre0"));
        assertEquals(otherRandData.getFeatures(0), modelToTune.feedForward(randData.getFeatures(), false).get("denseCentre0"));
        assertEquals(modelToTune.getLayer("denseRight0").params(), modelNow.getLayer("denseRight0").params());
        assertEquals(modelToTune.getLayer("outRight").params(), modelNow.getLayer("outRight").params());
        assertEquals(modelToTune.getLayer("outCentre").params(), modelNow.getLayer("outCentre").params());
        n++;
    }
}

Example 5 with MultiDataSet

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

the class TransferLearningComplex method testAddOutput.

@Test
public void testAddOutput() {
    NeuralNetConfiguration.Builder overallConf = new NeuralNetConfiguration.Builder().learningRate(0.9).activation(Activation.IDENTITY).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD);
    ComputationGraphConfiguration conf = overallConf.graphBuilder().addInputs("inCentre", "inRight").addLayer("denseCentre0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "inCentre").addLayer("denseRight0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "inRight").addVertex("mergeRight", new MergeVertex(), "denseCentre0", "denseRight0").addLayer("outRight", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(4).nOut(2).build(), "mergeRight").setOutputs("outRight").build();
    ComputationGraph modelToTune = new ComputationGraph(conf);
    modelToTune.init();
    ComputationGraph modelNow = new TransferLearning.GraphBuilder(modelToTune).addLayer("outCentre", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(2).nOut(3).build(), "denseCentre0").setOutputs("outCentre").build();
    assertEquals(2, modelNow.getNumOutputArrays());
    MultiDataSet rand = new MultiDataSet(new INDArray[] { Nd4j.rand(2, 2), Nd4j.rand(2, 2) }, new INDArray[] { Nd4j.rand(2, 2), Nd4j.rand(2, 3) });
    modelNow.fit(rand);
    log.info(modelNow.summary());
}