Examples with ComputationGraph org.deeplearning4j.nn.graph.ComputationGraph used on opensource projects

Example 21 with ComputationGraph

use of org.deeplearning4j.nn.graph.ComputationGraph in project deeplearning4j by deeplearning4j.

the class GradientCheckTestsComputationGraph method testBasicIrisWithElementWiseNode.

@Test
public void testBasicIrisWithElementWiseNode() {
    ElementWiseVertex.Op[] ops = new ElementWiseVertex.Op[] { ElementWiseVertex.Op.Add, ElementWiseVertex.Op.Subtract };
    for (ElementWiseVertex.Op op : ops) {
        Nd4j.getRandom().setSeed(12345);
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).learningRate(1.0).graphBuilder().addInputs("input").addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), "input").addLayer("l2", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.SIGMOID).build(), "input").addVertex("elementwise", new ElementWiseVertex(op), "l1", "l2").addLayer("outputLayer", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(5).nOut(3).build(), "elementwise").setOutputs("outputLayer").pretrain(false).backprop(true).build();
        ComputationGraph graph = new ComputationGraph(conf);
        graph.init();
        int numParams = (4 * 5 + 5) + (4 * 5 + 5) + (5 * 3 + 3);
        assertEquals(numParams, graph.numParams());
        Nd4j.getRandom().setSeed(12345);
        int nParams = graph.numParams();
        INDArray newParams = Nd4j.rand(1, nParams);
        graph.setParams(newParams);
        DataSet ds = new IrisDataSetIterator(150, 150).next();
        INDArray min = ds.getFeatureMatrix().min(0);
        INDArray max = ds.getFeatureMatrix().max(0);
        ds.getFeatureMatrix().subiRowVector(min).diviRowVector(max.sub(min));
        INDArray input = ds.getFeatureMatrix();
        INDArray labels = ds.getLabels();
        if (PRINT_RESULTS) {
            System.out.println("testBasicIrisWithElementWiseVertex(op=" + op + ")");
            for (int j = 0; j < graph.getNumLayers(); j++) System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
        }
        boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] { input }, new INDArray[] { labels });
        String msg = "testBasicIrisWithElementWiseVertex(op=" + op + ")";
        assertTrue(msg, gradOK);
    }
}

Example 22 with ComputationGraph

use of org.deeplearning4j.nn.graph.ComputationGraph in project deeplearning4j by deeplearning4j.

the class LayerConfigValidationTest method testCompGraphNullLayer.

@Test
public void testCompGraphNullLayer() {
    ComputationGraphConfiguration.GraphBuilder gb = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.01).iterations(3).seed(42).miniBatch(false).l1(0.2).l2(0.2).rmsDecay(0.3).regularization(true).updater(Updater.RMSPROP).graphBuilder().addInputs("in").addLayer("L" + 1, new GravesLSTM.Builder().nIn(20).updater(Updater.RMSPROP).nOut(10).weightInit(WeightInit.XAVIER).dropOut(0.4).l1(0.3).activation(Activation.SIGMOID).build(), "in").addLayer("output", new RnnOutputLayer.Builder().nIn(20).nOut(10).activation(Activation.SOFTMAX).weightInit(WeightInit.RELU_UNIFORM).build(), "L" + 1).setOutputs("output");
    ComputationGraphConfiguration conf = gb.build();
    ComputationGraph cg = new ComputationGraph(conf);
    cg.init();
}

Example 23 with ComputationGraph

use of org.deeplearning4j.nn.graph.ComputationGraph in project deeplearning4j by deeplearning4j.

the class CenterLossOutputLayerTest method testMNISTConfig.

@Test
//Should be run manually
@Ignore
public void testMNISTConfig() throws Exception {
    // Test batch size
    int batchSize = 64;
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
    ComputationGraph net = getCNNMnistConfig();
    net.init();
    net.setListeners(new ScoreIterationListener(1));
    for (int i = 0; i < 50; i++) {
        net.fit(mnistTrain.next());
        Thread.sleep(1000);
    }
    Thread.sleep(100000);
}

Example 24 with ComputationGraph

use of org.deeplearning4j.nn.graph.ComputationGraph 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 25 with ComputationGraph

use of org.deeplearning4j.nn.graph.ComputationGraph in project deeplearning4j by deeplearning4j.

the class TestGraphNodes method testDuplicateToTimeSeriesVertex.

@Test
public void testDuplicateToTimeSeriesVertex() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in2d", "in3d").addVertex("duplicateTS", new DuplicateToTimeSeriesVertex("in3d"), "in2d").addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).build(), "duplicateTS").setOutputs("out").build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    INDArray in2d = Nd4j.rand(3, 5);
    INDArray in3d = Nd4j.rand(new int[] { 3, 2, 7 });
    graph.setInputs(in2d, in3d);
    INDArray expOut = Nd4j.zeros(3, 5, 7);
    for (int i = 0; i < 7; i++) {
        expOut.put(new INDArrayIndex[] { NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(i) }, in2d);
    }
    GraphVertex gv = graph.getVertex("duplicateTS");
    gv.setInputs(in2d);
    INDArray outFwd = gv.doForward(true);
    assertEquals(expOut, outFwd);
    INDArray expOutBackward = expOut.sum(2);
    gv.setEpsilon(expOut);
    INDArray outBwd = gv.doBackward(false).getSecond()[0];
    assertEquals(expOutBackward, outBwd);
    String json = conf.toJson();
    ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
    assertEquals(conf, conf2);
}