Examples with ConvolutionLayer org.deeplearning4j.nn.conf.layers.ConvolutionLayer used on opensource projects

Example 1 with ConvolutionLayer

use of org.deeplearning4j.nn.conf.layers.ConvolutionLayer in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method getCNNConfig.

//////////////////////////////////////////////////////////////////////////////////
private static Layer getCNNConfig(int nIn, int nOut, int[] kernelSize, int[] stride, int[] padding) {
    ConvolutionLayer layer = new ConvolutionLayer.Builder(kernelSize, stride, padding).nIn(nIn).nOut(nOut).activation(Activation.SIGMOID).build();
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().iterations(1).layer(layer).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    return conf.getLayer().instantiate(conf, null, 0, params, true);
}

Example 2 with ConvolutionLayer

use of org.deeplearning4j.nn.conf.layers.ConvolutionLayer in project deeplearning4j by deeplearning4j.

the class TestConvolutionModes method testGlobalLocalConfig.

@Test
public void testGlobalLocalConfig() {
    for (ConvolutionMode cm : new ConvolutionMode[] { ConvolutionMode.Strict, ConvolutionMode.Truncate }) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).convolutionMode(cm).list().layer(0, new ConvolutionLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(1, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Strict).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(2, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Truncate).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(3, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Same).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(4, new SubsamplingLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(5, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Strict).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(6, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Truncate).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(7, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Same).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(8, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nOut(3).build()).build();
        assertEquals(cm, ((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Strict, ((ConvolutionLayer) conf.getConf(1).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Truncate, ((ConvolutionLayer) conf.getConf(2).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Same, ((ConvolutionLayer) conf.getConf(3).getLayer()).getConvolutionMode());
        assertEquals(cm, ((SubsamplingLayer) conf.getConf(4).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Strict, ((SubsamplingLayer) conf.getConf(5).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Truncate, ((SubsamplingLayer) conf.getConf(6).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Same, ((SubsamplingLayer) conf.getConf(7).getLayer()).getConvolutionMode());
    }
}

Example 3 with ConvolutionLayer

use of org.deeplearning4j.nn.conf.layers.ConvolutionLayer in project deeplearning4j by deeplearning4j.

the class TrainModule method getLayerInfoTable.

private String[][] getLayerInfoTable(int layerIdx, TrainModuleUtils.GraphInfo gi, I18N i18N, boolean noData, StatsStorage ss, String wid) {
    List<String[]> layerInfoRows = new ArrayList<>();
    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerName"), gi.getVertexNames().get(layerIdx) });
    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerType"), "" });
    if (!noData) {
        Persistable p = ss.getStaticInfo(currentSessionID, StatsListener.TYPE_ID, wid);
        if (p != null) {
            StatsInitializationReport initReport = (StatsInitializationReport) p;
            String configJson = initReport.getModelConfigJson();
            String modelClass = initReport.getModelClassName();
            //TODO error handling...
            String layerType = "";
            Layer layer = null;
            NeuralNetConfiguration nnc = null;
            if (modelClass.endsWith("MultiLayerNetwork")) {
                MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(configJson);
                //-1 because of input
                int confIdx = layerIdx - 1;
                if (confIdx >= 0) {
                    nnc = conf.getConf(confIdx);
                    layer = nnc.getLayer();
                } else {
                    //Input layer
                    layerType = "Input";
                }
            } else if (modelClass.endsWith("ComputationGraph")) {
                ComputationGraphConfiguration conf = ComputationGraphConfiguration.fromJson(configJson);
                String vertexName = gi.getVertexNames().get(layerIdx);
                Map<String, GraphVertex> vertices = conf.getVertices();
                if (vertices.containsKey(vertexName) && vertices.get(vertexName) instanceof LayerVertex) {
                    LayerVertex lv = (LayerVertex) vertices.get(vertexName);
                    nnc = lv.getLayerConf();
                    layer = nnc.getLayer();
                } else if (conf.getNetworkInputs().contains(vertexName)) {
                    layerType = "Input";
                } else {
                    GraphVertex gv = conf.getVertices().get(vertexName);
                    if (gv != null) {
                        layerType = gv.getClass().getSimpleName();
                    }
                }
            } else if (modelClass.endsWith("VariationalAutoencoder")) {
                layerType = gi.getVertexTypes().get(layerIdx);
                Map<String, String> map = gi.getVertexInfo().get(layerIdx);
                for (Map.Entry<String, String> entry : map.entrySet()) {
                    layerInfoRows.add(new String[] { entry.getKey(), entry.getValue() });
                }
            }
            if (layer != null) {
                layerType = getLayerType(layer);
            }
            if (layer != null) {
                String activationFn = null;
                if (layer instanceof FeedForwardLayer) {
                    FeedForwardLayer ffl = (FeedForwardLayer) layer;
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerNIn"), String.valueOf(ffl.getNIn()) });
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerSize"), String.valueOf(ffl.getNOut()) });
                    activationFn = layer.getActivationFn().toString();
                }
                int nParams = layer.initializer().numParams(nnc);
                layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerNParams"), String.valueOf(nParams) });
                if (nParams > 0) {
                    WeightInit wi = layer.getWeightInit();
                    String str = wi.toString();
                    if (wi == WeightInit.DISTRIBUTION) {
                        str += layer.getDist();
                    }
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerWeightInit"), str });
                    Updater u = layer.getUpdater();
                    String us = (u == null ? "" : u.toString());
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerUpdater"), us });
                //TODO: Maybe L1/L2, dropout, updater-specific values etc
                }
                if (layer instanceof ConvolutionLayer || layer instanceof SubsamplingLayer) {
                    int[] kernel;
                    int[] stride;
                    int[] padding;
                    if (layer instanceof ConvolutionLayer) {
                        ConvolutionLayer cl = (ConvolutionLayer) layer;
                        kernel = cl.getKernelSize();
                        stride = cl.getStride();
                        padding = cl.getPadding();
                    } else {
                        SubsamplingLayer ssl = (SubsamplingLayer) layer;
                        kernel = ssl.getKernelSize();
                        stride = ssl.getStride();
                        padding = ssl.getPadding();
                        activationFn = null;
                        layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerSubsamplingPoolingType"), ssl.getPoolingType().toString() });
                    }
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerCnnKernel"), Arrays.toString(kernel) });
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerCnnStride"), Arrays.toString(stride) });
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerCnnPadding"), Arrays.toString(padding) });
                }
                if (activationFn != null) {
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerActivationFn"), activationFn });
                }
            }
            layerInfoRows.get(1)[1] = layerType;
        }
    }
    return layerInfoRows.toArray(new String[layerInfoRows.size()][0]);
}

Example 4 with ConvolutionLayer

use of org.deeplearning4j.nn.conf.layers.ConvolutionLayer in project deeplearning4j by deeplearning4j.

the class CNNGradientCheckTest method testCnnMultiLayer.

@Test
public void testCnnMultiLayer() {
    int nOut = 2;
    int[] minibatchSizes = { 1, 2, 5 };
    int width = 5;
    int height = 5;
    int[] inputDepths = { 1, 2, 4 };
    String[] activations = { "sigmoid", "tanh" };
    SubsamplingLayer.PoolingType[] poolingTypes = new SubsamplingLayer.PoolingType[] { SubsamplingLayer.PoolingType.MAX, SubsamplingLayer.PoolingType.AVG };
    Nd4j.getRandom().setSeed(12345);
    for (int inputDepth : inputDepths) {
        for (String afn : activations) {
            for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
                for (int minibatchSize : minibatchSizes) {
                    INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
                    INDArray labels = Nd4j.zeros(minibatchSize, nOut);
                    for (int i = 0; i < minibatchSize; i++) {
                        labels.putScalar(new int[] { i, i % nOut }, 1.0);
                    }
                    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).regularization(false).learningRate(1.0).updater(Updater.SGD).activation(afn).list().layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0).nIn(inputDepth).nOut(2).build()).layer(1, new ConvolutionLayer.Builder().nIn(2).nOut(2).kernelSize(2, 2).stride(1, 1).padding(0, 0).build()).layer(2, new ConvolutionLayer.Builder().nIn(2).nOut(2).kernelSize(2, 2).stride(1, 1).padding(0, 0).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(2 * 2 * 2).nOut(nOut).build()).setInputType(InputType.convolutionalFlat(height, width, inputDepth)).build();
                    assertEquals(ConvolutionMode.Truncate, ((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
                    MultiLayerNetwork net = new MultiLayerNetwork(conf);
                    net.init();
                    for (int i = 0; i < 4; i++) {
                        System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
                    }
                    String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn=" + afn;
                    System.out.println(msg);
                    boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
                    assertTrue(msg, gradOK);
                }
            }
        }
    }
}

Example 5 with ConvolutionLayer

use of org.deeplearning4j.nn.conf.layers.ConvolutionLayer in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerSetupTest method testMultiChannel.

@Test
public void testMultiChannel() throws Exception {
    INDArray in = Nd4j.rand(new int[] { 10, 3, 28, 28 });
    INDArray labels = Nd4j.rand(10, 2);
    DataSet next = new DataSet(in, labels);
    NeuralNetConfiguration.ListBuilder builder = (NeuralNetConfiguration.ListBuilder) incompleteLFW();
    new ConvolutionLayerSetup(builder, 28, 28, 3);
    MultiLayerConfiguration conf = builder.build();
    ConvolutionLayer layer2 = (ConvolutionLayer) conf.getConf(2).getLayer();
    assertEquals(6, layer2.getNIn());
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.fit(next);
}