Examples with ComputationGraphConfiguration org.deeplearning4j.nn.conf.ComputationGraphConfiguration used on opensource projects

Example 26 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration in project deeplearning4j by deeplearning4j.

the class TestFlowListener method testUICG.

@Test
public void testUICG() throws Exception {
    // Number of input channels
    int nChannels = 1;
    // The number of possible outcomes
    int outputNum = 10;
    // Test batch size
    int batchSize = 64;
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
    ComputationGraphConfiguration conf = // Training iterations as above
    new NeuralNetConfiguration.Builder().seed(12345).iterations(1).regularization(true).l2(0.0005).learningRate(0.01).weightInit(WeightInit.XAVIER).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.NESTEROVS).momentum(0.9).graphBuilder().addInputs("in").addLayer("0", new ConvolutionLayer.Builder(5, 5).nIn(nChannels).stride(1, 1).nOut(20).activation(Activation.IDENTITY).build(), "in").addLayer("1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2).build(), "0").addLayer("2", new ConvolutionLayer.Builder(5, 5).stride(1, 1).nOut(50).activation(Activation.IDENTITY).build(), "1").addLayer("3", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2).build(), "2").addLayer("4", new DenseLayer.Builder().activation(Activation.RELU).nOut(500).build(), "3").addLayer("5", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum).activation(Activation.SOFTMAX).build(), "4").setOutputs("5").setInputTypes(InputType.convolutionalFlat(28, 28, 1)).backprop(true).pretrain(false).build();
    ComputationGraph net = new ComputationGraph(conf);
    net.init();
    net.setListeners(new FlowIterationListener(1), new ScoreIterationListener(1));
    for (int i = 0; i < 50; i++) {
        net.fit(mnistTrain.next());
        Thread.sleep(1000);
    }
    Thread.sleep(100000);
}

Example 27 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration 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 28 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration 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 29 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration 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 30 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration 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());
}