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

Example 66 with ComputationGraphConfiguration

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

the class TestCustomLayers method testCustomOutputLayerCG.

@Test
public void testCustomOutputLayerCG() {
    //Create a ComputationGraphConfiguration with custom output layer, and check JSON and YAML config actually works...
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).graphBuilder().addInputs("in").addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").addLayer("1", new CustomOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build(), "0").setOutputs("1").pretrain(false).backprop(true).build();
    String json = conf.toJson();
    String yaml = conf.toYaml();
    System.out.println(json);
    ComputationGraphConfiguration confFromJson = ComputationGraphConfiguration.fromJson(json);
    assertEquals(conf, confFromJson);
    ComputationGraphConfiguration confFromYaml = ComputationGraphConfiguration.fromYaml(yaml);
    assertEquals(conf, confFromYaml);
    //Third: check initialization
    Nd4j.getRandom().setSeed(12345);
    ComputationGraph net = new ComputationGraph(conf);
    net.init();
    assertTrue(net.getLayer(1) instanceof CustomOutputLayerImpl);
    //Fourth: compare to an equivalent standard output layer (should be identical)
    ComputationGraphConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).graphBuilder().addInputs("in").addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").addLayer("1", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build(), "0").setOutputs("1").pretrain(false).backprop(true).build();
    Nd4j.getRandom().setSeed(12345);
    ComputationGraph net2 = new ComputationGraph(conf2);
    net2.init();
    assertEquals(net2.params(), net.params());
    INDArray testFeatures = Nd4j.rand(1, 10);
    INDArray testLabels = Nd4j.zeros(1, 10);
    testLabels.putScalar(0, 3, 1.0);
    DataSet ds = new DataSet(testFeatures, testLabels);
    assertEquals(net2.output(testFeatures)[0], net.output(testFeatures)[0]);
    assertEquals(net2.score(ds), net.score(ds), 1e-6);
}

Example 67 with ComputationGraphConfiguration

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

the class CenterLossOutputLayerTest method getCNNMnistConfig.

public ComputationGraph getCNNMnistConfig() {
    // Number of input channels
    int nChannels = 1;
    // The number of possible outcomes
    int outputNum = 10;
    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("input").setInputTypes(InputType.convolutionalFlat(28, 28, 1)).addLayer("0", new ConvolutionLayer.Builder(5, 5).nIn(nChannels).stride(1, 1).nOut(20).activation(Activation.IDENTITY).build(), "input").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("output", new org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder(LossFunction.MCXENT).nOut(outputNum).activation(Activation.SOFTMAX).build(), "4").setOutputs("output").build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    return graph;
}

Example 68 with ComputationGraphConfiguration

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

the class TestGraphNodes method testLastTimeStepVertex.

@Test
public void testLastTimeStepVertex() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in").addVertex("lastTS", new LastTimeStepVertex("in"), "in").addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).build(), "lastTS").setOutputs("out").build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    //First: test without input mask array
    Nd4j.getRandom().setSeed(12345);
    INDArray in = Nd4j.rand(new int[] { 3, 5, 6 });
    INDArray expOut = in.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(5));
    GraphVertex gv = graph.getVertex("lastTS");
    gv.setInputs(in);
    //Forward pass:
    INDArray outFwd = gv.doForward(true);
    assertEquals(expOut, outFwd);
    //Backward pass:
    gv.setEpsilon(expOut);
    Pair<Gradient, INDArray[]> pair = gv.doBackward(false);
    INDArray eps = pair.getSecond()[0];
    assertArrayEquals(in.shape(), eps.shape());
    assertEquals(Nd4j.zeros(3, 5, 5), eps.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4, true)));
    assertEquals(expOut, eps.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(5)));
    //Second: test with input mask array
    INDArray inMask = Nd4j.zeros(3, 6);
    inMask.putRow(0, Nd4j.create(new double[] { 1, 1, 1, 0, 0, 0 }));
    inMask.putRow(1, Nd4j.create(new double[] { 1, 1, 1, 1, 0, 0 }));
    inMask.putRow(2, Nd4j.create(new double[] { 1, 1, 1, 1, 1, 0 }));
    graph.setLayerMaskArrays(new INDArray[] { inMask }, null);
    expOut = Nd4j.zeros(3, 5);
    expOut.putRow(0, in.get(NDArrayIndex.point(0), NDArrayIndex.all(), NDArrayIndex.point(2)));
    expOut.putRow(1, in.get(NDArrayIndex.point(1), NDArrayIndex.all(), NDArrayIndex.point(3)));
    expOut.putRow(2, in.get(NDArrayIndex.point(2), NDArrayIndex.all(), NDArrayIndex.point(4)));
    gv.setInputs(in);
    outFwd = gv.doForward(true);
    assertEquals(expOut, outFwd);
    String json = conf.toJson();
    ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
    assertEquals(conf, conf2);
}

Example 69 with ComputationGraphConfiguration

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

the class TestGraphNodes method testJSON.

@Test
public void testJSON() {
    //The config here is non-sense, but that doesn't matter for config -> json -> config test
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in").addVertex("v1", new ElementWiseVertex(ElementWiseVertex.Op.Add), "in").addVertex("v2", new org.deeplearning4j.nn.conf.graph.MergeVertex(), "in", "in").addVertex("v3", new PreprocessorVertex(new CnnToFeedForwardPreProcessor(1, 2, 1)), "in").addVertex("v4", new org.deeplearning4j.nn.conf.graph.SubsetVertex(0, 1), "in").addVertex("v5", new DuplicateToTimeSeriesVertex("in"), "in").addVertex("v6", new LastTimeStepVertex("in"), "in").addVertex("v7", new org.deeplearning4j.nn.conf.graph.StackVertex(), "in").addVertex("v8", new org.deeplearning4j.nn.conf.graph.UnstackVertex(0, 1), "in").addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in").setOutputs("out").build();
    String json = conf.toJson();
    ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
    assertEquals(conf, conf2);
}

Example 70 with ComputationGraphConfiguration

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

the class CenterLossOutputLayerTest method getGraph.

private ComputationGraph getGraph(int numLabels, double lambda) {
    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("input1").addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.RELU).build(), "input1").addLayer("lossLayer", new CenterLossOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(numLabels).lambda(lambda).activation(Activation.SOFTMAX).build(), "l1").setOutputs("lossLayer").pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    return graph;
}