Examples with MultiLayerNetwork org.deeplearning4j.nn.multilayer.MultiLayerNetwork used on opensource projects

Example 66 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class VaeGradientCheckTests method testVaeAsMLP.

@Test
public void testVaeAsMLP() {
    //Post pre-training: a VAE can be used as a MLP, by taking the mean value from p(z|x) as the output
    //This gradient check tests this part
    //activation functions such as relu and hardtanh: may randomly fail due to discontinuities
    String[] activFns = { "identity", "tanh" };
    LossFunction[] lossFunctions = { LossFunction.MCXENT, LossFunction.MSE };
    //i.e., lossFunctions[i] used with outputActivations[i] here
    String[] outputActivations = { "softmax", "tanh" };
    //use l2vals[i] with l1vals[i]
    double[] l2vals = { 0.4, 0.0, 0.4, 0.4 };
    double[] l1vals = { 0.0, 0.0, 0.5, 0.0 };
    double[] biasL2 = { 0.0, 0.0, 0.0, 0.2 };
    double[] biasL1 = { 0.0, 0.0, 0.6, 0.0 };
    int[][] encoderLayerSizes = new int[][] { { 5 }, { 5, 6 } };
    int[][] decoderLayerSizes = new int[][] { { 6 }, { 7, 8 } };
    Nd4j.getRandom().setSeed(12345);
    for (int minibatch : new int[] { 1, 5 }) {
        INDArray input = Nd4j.rand(minibatch, 4);
        INDArray labels = Nd4j.create(minibatch, 3);
        for (int i = 0; i < minibatch; i++) {
            labels.putScalar(i, i % 3, 1.0);
        }
        for (int ls = 0; ls < encoderLayerSizes.length; ls++) {
            int[] encoderSizes = encoderLayerSizes[ls];
            int[] decoderSizes = decoderLayerSizes[ls];
            for (String afn : activFns) {
                for (int i = 0; i < lossFunctions.length; i++) {
                    for (int k = 0; k < l2vals.length; k++) {
                        LossFunction lf = lossFunctions[i];
                        String outputActivation = outputActivations[i];
                        double l2 = l2vals[k];
                        double l1 = l1vals[k];
                        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(true).l2(l2).l1(l1).l2Bias(biasL2[k]).l1Bias(biasL1[k]).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(1.0).seed(12345L).list().layer(0, new VariationalAutoencoder.Builder().nIn(4).nOut(3).encoderLayerSizes(encoderSizes).decoderLayerSizes(decoderSizes).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).activation(afn).updater(Updater.SGD).build()).layer(1, new OutputLayer.Builder(lf).activation(outputActivation).nIn(3).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.SGD).build()).pretrain(false).backprop(true).build();
                        MultiLayerNetwork mln = new MultiLayerNetwork(conf);
                        mln.init();
                        String msg = "testVaeAsMLP() - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", encLayerSizes = " + Arrays.toString(encoderSizes) + ", decLayerSizes = " + Arrays.toString(decoderSizes) + ", l2=" + l2 + ", l1=" + l1;
                        if (PRINT_RESULTS) {
                            System.out.println(msg);
                            for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
                        }
                        boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
                        assertTrue(msg, gradOK);
                    }
                }
            }
        }
    }
}

Example 67 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class MultiLayerNeuralNetConfigurationTest method testListOverloads.

@Test
public void testListOverloads() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).list().layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build()).layer(1, new OutputLayer.Builder().nIn(4).nOut(5).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    DenseLayer dl = (DenseLayer) conf.getConf(0).getLayer();
    assertEquals(3, dl.getNIn());
    assertEquals(4, dl.getNOut());
    OutputLayer ol = (OutputLayer) conf.getConf(1).getLayer();
    assertEquals(4, ol.getNIn());
    assertEquals(5, ol.getNOut());
    MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345).list().layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build()).layer(1, new OutputLayer.Builder().nIn(4).nOut(5).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
    net2.init();
    MultiLayerConfiguration conf3 = new NeuralNetConfiguration.Builder().seed(12345).list(new DenseLayer.Builder().nIn(3).nOut(4).build(), new OutputLayer.Builder().nIn(4).nOut(5).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net3 = new MultiLayerNetwork(conf3);
    net3.init();
    assertEquals(conf, conf2);
    assertEquals(conf, conf3);
}

Example 68 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class MultiLayerNeuralNetConfigurationTest method testInvalidConfig.

@Test
public void testInvalidConfig() {
    try {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).list().pretrain(false).backprop(true).build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        fail("No exception thrown for invalid configuration");
    } catch (IllegalStateException e) {
        //OK
        e.printStackTrace();
    } catch (Throwable e) {
        e.printStackTrace();
        fail("Unexpected exception thrown for invalid config");
    }
    try {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).list().layer(1, new DenseLayer.Builder().nIn(3).nOut(4).build()).layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build()).pretrain(false).backprop(true).build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        fail("No exception thrown for invalid configuration");
    } catch (IllegalStateException e) {
        //OK
        e.printStackTrace();
    } catch (Throwable e) {
        e.printStackTrace();
        fail("Unexpected exception thrown for invalid config");
    }
    try {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).list().layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build()).layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build()).pretrain(false).backprop(true).build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        fail("No exception thrown for invalid configuration");
    } catch (IllegalStateException e) {
        //OK
        e.printStackTrace();
    } catch (Throwable e) {
        e.printStackTrace();
        fail("Unexpected exception thrown for invalid config");
    }
}

Example 69 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class MultiNeuralNetConfLayerBuilderTest method testRbmSetup.

@Test
public void testRbmSetup() throws Exception {
    MultiLayerConfiguration multiLayerConfiguration = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).seed(123).iterations(5).maxNumLineSearchIterations(// Magical Optimisation Stuff
    10).regularization(true).list().layer(0, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN).nIn(784).nOut(1000).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()).layer(1, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN).nIn(1000).nOut(500).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()).layer(2, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN).nIn(500).nOut(250).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(250).nOut(10).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork network = new MultiLayerNetwork(multiLayerConfiguration);
    network.init();
    DataSet d = new MnistDataSetIterator(2, 2).next();
    org.deeplearning4j.nn.api.Layer firstRbm = network.getLayer(0);
    org.deeplearning4j.nn.api.Layer secondRbm = network.getLayer(1);
    org.deeplearning4j.nn.api.Layer thirdRbm = network.getLayer(2);
    org.deeplearning4j.nn.api.Layer fourthRbm = network.getLayer(3);
    INDArray[] weightMatrices = new INDArray[] { firstRbm.getParam(DefaultParamInitializer.WEIGHT_KEY), secondRbm.getParam(DefaultParamInitializer.WEIGHT_KEY), thirdRbm.getParam(DefaultParamInitializer.WEIGHT_KEY), fourthRbm.getParam(DefaultParamInitializer.WEIGHT_KEY) };
    INDArray[] hiddenBiases = new INDArray[] { firstRbm.getParam(DefaultParamInitializer.BIAS_KEY), secondRbm.getParam(DefaultParamInitializer.BIAS_KEY), thirdRbm.getParam(DefaultParamInitializer.BIAS_KEY), fourthRbm.getParam(DefaultParamInitializer.BIAS_KEY) };
    int[][] shapeAssertions = new int[][] { { 784, 1000 }, { 1000, 500 }, { 500, 250 }, { 250, 10 } };
    int[][] biasAssertions = new int[][] { { 1, 1000 }, { 1, 500 }, { 1, 250 }, { 1, 10 } };
    for (int i = 0; i < shapeAssertions.length; i++) {
        assertArrayEquals(shapeAssertions[i], weightMatrices[i].shape());
        assertArrayEquals(biasAssertions[i], hiddenBiases[i].shape());
    }
    network.fit(d);
}

Example 70 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class GradientCheckTests method testGradientGravesBidirectionalLSTMEdgeCases.

@Test
public void testGradientGravesBidirectionalLSTMEdgeCases() {
    //Edge cases: T=1, miniBatchSize=1, both
    int[] timeSeriesLength = { 1, 5, 1 };
    int[] miniBatchSize = { 7, 1, 1 };
    int nIn = 7;
    int layerSize = 9;
    int nOut = 4;
    for (int i = 0; i < timeSeriesLength.length; i++) {
        Random r = new Random(12345L);
        INDArray input = Nd4j.zeros(miniBatchSize[i], nIn, timeSeriesLength[i]);
        for (int m = 0; m < miniBatchSize[i]; m++) {
            for (int j = 0; j < nIn; j++) {
                for (int k = 0; k < timeSeriesLength[i]; k++) {
                    input.putScalar(new int[] { m, j, k }, r.nextDouble() - 0.5);
                }
            }
        }
        INDArray labels = Nd4j.zeros(miniBatchSize[i], nOut, timeSeriesLength[i]);
        for (int m = 0; m < miniBatchSize[i]; m++) {
            for (int j = 0; j < timeSeriesLength[i]; j++) {
                int idx = r.nextInt(nOut);
                labels.putScalar(new int[] { m, idx, j }, 1.0f);
            }
        }
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).seed(12345L).list().layer(0, new GravesBidirectionalLSTM.Builder().nIn(nIn).nOut(layerSize).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).build()).layer(1, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).build()).pretrain(false).backprop(true).build();
        MultiLayerNetwork mln = new MultiLayerNetwork(conf);
        mln.init();
        boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
        String msg = "testGradientGravesLSTMEdgeCases() - timeSeriesLength=" + timeSeriesLength[i] + ", miniBatchSize=" + miniBatchSize[i];
        assertTrue(msg, gradOK);
    }
}