Java MultiLayerConfiguration.getConfs方法代码示例

import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入方法依赖的package包/类
/**
 * Convert a MultiLayerConfiguration into a Computation graph
 *
 * @param mlc Layer-wise configuration
 * @param shape Inputshape
 * @return ComputationGraph based on the configuration in the MLC
 */
default ComputationGraph mlpToCG(MultiLayerConfiguration mlc, int[][] shape) {
  ComputationGraphConfiguration.GraphBuilder builder =
      new NeuralNetConfiguration.Builder()
          .trainingWorkspaceMode(WorkspaceMode.SEPARATE)
          .inferenceWorkspaceMode(WorkspaceMode.SEPARATE)
          .graphBuilder();
  List<NeuralNetConfiguration> confs = mlc.getConfs();

  // Start with input
  String currentInput = "input";
  builder.addInputs(currentInput);

  // Iterate MLN configurations layer-wise
  for (NeuralNetConfiguration conf : confs) {
    Layer l = conf.getLayer();
    String lName = l.getLayerName();

    // Connect current layer with last layer
    builder.addLayer(lName, l, currentInput);
    currentInput = lName;
  }
  builder.setOutputs(currentInput);

  // Configure inputs
  builder.setInputTypes(InputType.convolutional(shape[0][1], shape[0][2], shape[0][0]));

  // Build
  ComputationGraphConfiguration cgc = builder.build();
  return new ComputationGraph(cgc);
}
 

import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入方法依赖的package包/类
public static GraphInfo buildGraphInfo(MultiLayerConfiguration config) {
    List<String> vertexNames = new ArrayList<>();
    List<String> originalVertexName = new ArrayList<>();
    List<String> layerTypes = new ArrayList<>();
    List<List<Integer>> layerInputs = new ArrayList<>();
    List<Map<String, String>> layerInfo = new ArrayList<>();
    vertexNames.add("Input");
    originalVertexName.add(null);
    layerTypes.add("Input");
    layerInputs.add(Collections.emptyList());
    layerInfo.add(Collections.emptyMap());


    List<NeuralNetConfiguration> list = config.getConfs();
    int layerIdx = 1;
    for (NeuralNetConfiguration c : list) {
        Layer layer = c.getLayer();
        String layerName = layer.getLayerName();
        if (layerName == null)
            layerName = "layer" + layerIdx;
        vertexNames.add(layerName);
        originalVertexName.add(String.valueOf(layerIdx - 1));

        String layerType = c.getLayer().getClass().getSimpleName().replaceAll("Layer$", "");
        layerTypes.add(layerType);

        layerInputs.add(Collections.singletonList(layerIdx - 1));
        layerIdx++;

        //Extract layer info
        Map<String, String> map = getLayerInfo(c, layer);
        layerInfo.add(map);
    }

    return new GraphInfo(vertexNames, layerTypes, layerInputs, layerInfo, originalVertexName);
}
 

import org.deeplearning4j.nn.conf.MultiLayerConfiguration; //导入方法依赖的package包/类
/**
 * Convert a MultiLayerNetwork to a ComputationGraph
 *
 * @return ComputationGraph equivalent to this network (including parameters and updater state)
 */
public static ComputationGraph toComputationGraph(MultiLayerNetwork net) {

    //We rely heavily here on the fact that the topological sort order - and hence the layout of parameters - is
    // by definition the identical for a MLN and "single stack" computation graph. This also has to hold
    // for the updater state...

    ComputationGraphConfiguration.GraphBuilder b = new NeuralNetConfiguration.Builder()
            .graphBuilder();

    MultiLayerConfiguration origConf = net.getLayerWiseConfigurations().clone();


    int layerIdx = 0;
    String lastLayer = "in";
    b.addInputs("in");
    for (NeuralNetConfiguration c : origConf.getConfs()) {
        String currLayer = String.valueOf(layerIdx);

        InputPreProcessor preproc = origConf.getInputPreProcess(layerIdx);
        b.addLayer(currLayer, c.getLayer(), preproc, lastLayer);

        lastLayer = currLayer;
        layerIdx++;
    }
    b.setOutputs(lastLayer);

    ComputationGraphConfiguration conf = b.build();

    ComputationGraph cg = new ComputationGraph(conf);
    cg.init();

    cg.setParams(net.params());

    //Also copy across updater state:
    INDArray updaterState = net.getUpdater().getStateViewArray();
    if (updaterState != null) {
        cg.getUpdater().getUpdaterStateViewArray()
                .assign(updaterState);
    }

    return cg;
}