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

Example 31 with ComputationGraphConfiguration

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

the class KerasModel method getComputationGraphConfiguration.

/**
     * Configure a ComputationGraph from this Keras Model configuration.
     *
     * @return          ComputationGraph
     */
public ComputationGraphConfiguration getComputationGraphConfiguration() throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
    if (!this.className.equals(MODEL_CLASS_NAME_MODEL) && !this.className.equals(MODEL_CLASS_NAME_SEQUENTIAL))
        throw new InvalidKerasConfigurationException("Keras model class name " + this.className + " incompatible with ComputationGraph");
    NeuralNetConfiguration.Builder modelBuilder = new NeuralNetConfiguration.Builder();
    ComputationGraphConfiguration.GraphBuilder graphBuilder = modelBuilder.graphBuilder();
    /* Build String array of input layer names, add to ComputationGraph. */
    String[] inputLayerNameArray = new String[this.inputLayerNames.size()];
    this.inputLayerNames.toArray(inputLayerNameArray);
    graphBuilder.addInputs(inputLayerNameArray);
    /* Build InputType array of input layer types, add to ComputationGraph. */
    List<InputType> inputTypeList = new ArrayList<InputType>();
    for (String inputLayerName : this.inputLayerNames) inputTypeList.add(this.layers.get(inputLayerName).getOutputType());
    InputType[] inputTypes = new InputType[inputTypeList.size()];
    inputTypeList.toArray(inputTypes);
    graphBuilder.setInputTypes(inputTypes);
    /* Build String array of output layer names, add to ComputationGraph. */
    String[] outputLayerNameArray = new String[this.outputLayerNames.size()];
    this.outputLayerNames.toArray(outputLayerNameArray);
    graphBuilder.setOutputs(outputLayerNameArray);
    Map<String, InputPreProcessor> preprocessors = new HashMap<String, InputPreProcessor>();
    /* Add layersOrdered one at a time. */
    for (KerasLayer layer : this.layersOrdered) {
        /* Get inbound layer names. */
        List<String> inboundLayerNames = layer.getInboundLayerNames();
        String[] inboundLayerNamesArray = new String[inboundLayerNames.size()];
        inboundLayerNames.toArray(inboundLayerNamesArray);
        /* Get inbound InputTypes and InputPreProcessor, if necessary. */
        List<InputType> inboundTypeList = new ArrayList<InputType>();
        for (String layerName : inboundLayerNames) inboundTypeList.add(this.outputTypes.get(layerName));
        InputType[] inboundTypeArray = new InputType[inboundTypeList.size()];
        inboundTypeList.toArray(inboundTypeArray);
        InputPreProcessor preprocessor = layer.getInputPreprocessor(inboundTypeArray);
        if (layer.usesRegularization())
            modelBuilder.setUseRegularization(true);
        if (layer.isLayer()) {
            /* Add DL4J layer. */
            if (preprocessor != null)
                preprocessors.put(layer.getLayerName(), preprocessor);
            graphBuilder.addLayer(layer.getLayerName(), layer.getLayer(), inboundLayerNamesArray);
            if (this.outputLayerNames.contains(layer.getLayerName()) && !(layer.getLayer() instanceof IOutputLayer))
                log.warn("Model cannot be trained: output layer " + layer.getLayerName() + " is not an IOutputLayer (no loss function specified)");
        } else if (layer.isVertex()) {
            /* Add DL4J vertex. */
            if (preprocessor != null)
                preprocessors.put(layer.getLayerName(), preprocessor);
            graphBuilder.addVertex(layer.getLayerName(), layer.getVertex(), inboundLayerNamesArray);
            if (this.outputLayerNames.contains(layer.getLayerName()) && !(layer.getVertex() instanceof IOutputLayer))
                log.warn("Model cannot be trained: output vertex " + layer.getLayerName() + " is not an IOutputLayer (no loss function specified)");
        } else if (layer.isInputPreProcessor()) {
            if (preprocessor == null)
                throw new UnsupportedKerasConfigurationException("Layer " + layer.getLayerName() + " could not be mapped to Layer, Vertex, or InputPreProcessor");
            graphBuilder.addVertex(layer.getLayerName(), new PreprocessorVertex(preprocessor), inboundLayerNamesArray);
        }
        if (this.outputLayerNames.contains(layer.getLayerName()))
            log.warn("Model cannot be trained: output " + layer.getLayerName() + " is not an IOutputLayer (no loss function specified)");
    }
    graphBuilder.setInputPreProcessors(preprocessors);
    /* Whether to use standard backprop (or BPTT) or truncated BPTT. */
    if (this.useTruncatedBPTT && this.truncatedBPTT > 0)
        graphBuilder.backpropType(BackpropType.TruncatedBPTT).tBPTTForwardLength(truncatedBPTT).tBPTTBackwardLength(truncatedBPTT);
    else
        graphBuilder.backpropType(BackpropType.Standard);
    return graphBuilder.build();
}

Example 32 with ComputationGraphConfiguration

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

the class ModelGuesserTest method testModelGuessConfig.

@Test
public void testModelGuessConfig() throws Exception {
    ClassPathResource resource = new ClassPathResource("modelimport/keras/configs/cnn_tf_config.json", ModelGuesserTest.class.getClassLoader());
    File f = getTempFile(resource);
    String configFilename = f.getAbsolutePath();
    Object conf = ModelGuesser.loadConfigGuess(configFilename);
    assertTrue(conf instanceof MultiLayerConfiguration);
    ClassPathResource sequenceResource = new ClassPathResource("/keras/simple/mlp_fapi_multiloss_config.json");
    File f2 = getTempFile(sequenceResource);
    Object sequenceConf = ModelGuesser.loadConfigGuess(f2.getAbsolutePath());
    assertTrue(sequenceConf instanceof ComputationGraphConfiguration);
    ClassPathResource resourceDl4j = new ClassPathResource("model.json");
    File fDl4j = getTempFile(resourceDl4j);
    String configFilenameDl4j = fDl4j.getAbsolutePath();
    Object confDl4j = ModelGuesser.loadConfigGuess(configFilenameDl4j);
    assertTrue(confDl4j instanceof ComputationGraphConfiguration);
}

Example 33 with ComputationGraphConfiguration

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

the class ModelSerializerTest method testWriteCGModelInputStream.

@Test
public void testWriteCGModelInputStream() throws Exception {
    ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.1).graphBuilder().addInputs("in").addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(2).build(), "in").addLayer("out", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).build(), "dense").setOutputs("out").pretrain(false).backprop(true).build();
    ComputationGraph cg = new ComputationGraph(config);
    cg.init();
    File tempFile = File.createTempFile("tsfs", "fdfsdf");
    tempFile.deleteOnExit();
    ModelSerializer.writeModel(cg, tempFile, true);
    FileInputStream fis = new FileInputStream(tempFile);
    ComputationGraph network = ModelSerializer.restoreComputationGraph(fis);
    assertEquals(network.getConfiguration().toJson(), cg.getConfiguration().toJson());
    assertEquals(cg.params(), network.params());
    assertEquals(cg.getUpdater(), network.getUpdater());
}

Example 34 with ComputationGraphConfiguration

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

the class TrainModule method getConfig.

private Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> getConfig() {
    boolean noData = currentSessionID == null;
    StatsStorage ss = (noData ? null : knownSessionIDs.get(currentSessionID));
    List<Persistable> allStatic = (noData ? Collections.EMPTY_LIST : ss.getAllStaticInfos(currentSessionID, StatsListener.TYPE_ID));
    if (allStatic.size() == 0)
        return null;
    StatsInitializationReport p = (StatsInitializationReport) allStatic.get(0);
    String modelClass = p.getModelClassName();
    String config = p.getModelConfigJson();
    if (modelClass.endsWith("MultiLayerNetwork")) {
        MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(config);
        return new Triple<>(conf, null, null);
    } else if (modelClass.endsWith("ComputationGraph")) {
        ComputationGraphConfiguration conf = ComputationGraphConfiguration.fromJson(config);
        return new Triple<>(null, conf, null);
    } else {
        try {
            NeuralNetConfiguration layer = NeuralNetConfiguration.mapper().readValue(config, NeuralNetConfiguration.class);
            return new Triple<>(null, null, layer);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    return null;
}

Example 35 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration 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]);
}