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

Example 51 with ComputationGraphConfiguration

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

the class GradientCheckTestsComputationGraph method testMultipleInputsLayer.

@Test
public void testMultipleInputsLayer() {
    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).activation(Activation.TANH).graphBuilder().addInputs("i0", "i1", "i2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0").addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i1").addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i2").addLayer("d3", new DenseLayer.Builder().nIn(6).nOut(2).build(), "d0", "d1", "d2").addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2).nOut(2).build(), "d3").setOutputs("out").pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    int[] minibatchSizes = { 1, 3 };
    for (int mb : minibatchSizes) {
        INDArray[] inputs = new INDArray[3];
        for (int i = 0; i < 3; i++) {
            inputs[i] = Nd4j.rand(mb, 2);
        }
        INDArray out = Nd4j.rand(mb, 2);
        String msg = "testMultipleInputsLayer() - minibatchSize = " + mb;
        if (PRINT_RESULTS) {
            System.out.println(msg);
            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, inputs, new INDArray[] { out });
        assertTrue(msg, gradOK);
    }
}

Example 52 with ComputationGraphConfiguration

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

the class GradientCheckTestsComputationGraph method testBasicIrisTripletStackingL2Loss.

@Test
public void testBasicIrisTripletStackingL2Loss() {
    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", "input2", "input3").addVertex("stack1", new StackVertex(), "input1", "input2", "input3").addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), "stack1").addVertex("unstack0", new UnstackVertex(0, 3), "l1").addVertex("unstack1", new UnstackVertex(1, 3), "l1").addVertex("unstack2", new UnstackVertex(2, 3), "l1").addVertex("l2-1", new L2Vertex(), "unstack1", // x - x-
    "unstack0").addVertex("l2-2", new L2Vertex(), "unstack1", // x - x+
    "unstack2").addLayer("lossLayer", new LossLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).build(), "l2-1", "l2-2").setOutputs("lossLayer").pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    int numParams = (4 * 5 + 5);
    assertEquals(numParams, graph.numParams());
    Nd4j.getRandom().setSeed(12345);
    int nParams = graph.numParams();
    INDArray newParams = Nd4j.rand(1, nParams);
    graph.setParams(newParams);
    INDArray pos = Nd4j.rand(150, 4);
    INDArray anc = Nd4j.rand(150, 4);
    INDArray neg = Nd4j.rand(150, 4);
    INDArray labels = Nd4j.zeros(150, 2);
    Random r = new Random(12345);
    for (int i = 0; i < 150; i++) {
        labels.putScalar(i, r.nextInt(2), 1.0);
    }
    Map<String, INDArray> out = graph.feedForward(new INDArray[] { pos, anc, neg }, true);
    for (String s : out.keySet()) {
        System.out.println(s + "\t" + Arrays.toString(out.get(s).shape()));
    }
    if (PRINT_RESULTS) {
        System.out.println("testBasicIrisTripletStackingL2Loss()");
        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[] { pos, anc, neg }, new INDArray[] { labels });
    String msg = "testBasicIrisTripletStackingL2Loss()";
    assertTrue(msg, gradOK);
}

Example 53 with ComputationGraphConfiguration

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

the class GradientCheckTestsComputationGraph method testBasicL2.

@Test
public void testBasicL2() {
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).activation(Activation.TANH).updater(Updater.NONE).learningRate(1.0).graphBuilder().addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1").addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2").addVertex("l2", new L2Vertex(), "d0", "d1").addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(1).nOut(1).activation(Activation.IDENTITY).build(), "l2").setOutputs("out").pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    Nd4j.getRandom().setSeed(12345);
    int nParams = graph.numParams();
    INDArray newParams = Nd4j.rand(1, nParams);
    graph.setParams(newParams);
    int[] mbSizes = new int[] { 1, 3, 10 };
    for (int minibatch : mbSizes) {
        INDArray in1 = Nd4j.rand(minibatch, 2);
        INDArray in2 = Nd4j.rand(minibatch, 2);
        INDArray labels = Nd4j.rand(minibatch, 1);
        String testName = "testBasicL2() - minibatch = " + minibatch;
        if (PRINT_RESULTS) {
            System.out.println(testName);
            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[] { in1, in2 }, new INDArray[] { labels });
        assertTrue(testName, gradOK);
    }
}

Example 54 with ComputationGraphConfiguration

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

the class GradientCheckTestsComputationGraph method testMultipleOutputsMergeCnn.

@Test
public void testMultipleOutputsMergeCnn() {
    int inH = 7;
    int inW = 7;
    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).activation(Activation.TANH).graphBuilder().addInputs("input").addLayer("l0", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0).nIn(2).nOut(2).activation(Activation.TANH).build(), "input").addLayer("l1", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0).nIn(2).nOut(2).activation(Activation.TANH).build(), "l0").addLayer("l2", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0).nIn(2).nOut(2).activation(Activation.TANH).build(), "l0").addVertex("m", new MergeVertex(), "l1", "l2").addLayer("l3", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0).nIn(4).nOut(2).activation(Activation.TANH).build(), "m").addLayer("l4", new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0).nIn(4).nOut(2).activation(Activation.TANH).build(), "m").addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nOut(2).build(), "l3", "l4").setOutputs("out").setInputTypes(InputType.convolutional(inH, inW, 2)).pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    int[] minibatchSizes = { 1, 3 };
    for (int mb : minibatchSizes) {
        //Order: examples, channels, height, width
        INDArray input = Nd4j.rand(new int[] { mb, 2, inH, inW });
        INDArray out = Nd4j.rand(mb, 2);
        String msg = "testMultipleOutputsMergeVertex() - minibatchSize = " + mb;
        if (PRINT_RESULTS) {
            System.out.println(msg);
            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[] { out });
        assertTrue(msg, gradOK);
    }
}

Example 55 with ComputationGraphConfiguration

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

the class GradientCheckTestsComputationGraph method testLSTMWithMerging.

@Test
public void testLSTMWithMerging() {
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0.2, 0.6)).updater(Updater.NONE).learningRate(1.0).graphBuilder().addInputs("input").setOutputs("out").addLayer("lstm1", new GravesLSTM.Builder().nIn(3).nOut(4).activation(Activation.TANH).build(), "input").addLayer("lstm2", new GravesLSTM.Builder().nIn(4).nOut(4).activation(Activation.TANH).build(), "lstm1").addLayer("dense1", new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.SIGMOID).build(), "lstm1").addLayer("lstm3", new GravesLSTM.Builder().nIn(4).nOut(4).activation(Activation.TANH).build(), "dense1").addVertex("merge", new MergeVertex(), "lstm2", "lstm3").addLayer("out", new RnnOutputLayer.Builder().nIn(8).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "merge").inputPreProcessor("dense1", new RnnToFeedForwardPreProcessor()).inputPreProcessor("lstm3", new FeedForwardToRnnPreProcessor()).pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    Random r = new Random(12345);
    INDArray input = Nd4j.rand(new int[] { 3, 3, 5 });
    INDArray labels = Nd4j.zeros(3, 3, 5);
    for (int i = 0; i < 3; i++) {
        for (int j = 0; j < 5; j++) {
            labels.putScalar(new int[] { i, r.nextInt(3), j }, 1.0);
        }
    }
    if (PRINT_RESULTS) {
        System.out.println("testLSTMWithMerging()");
        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 = "testLSTMWithMerging()";
    assertTrue(msg, gradOK);
}