本文整理了Java中org.deeplearning4j.nn.multilayer.MultiLayerNetwork.getLayers()方法的一些代码示例，展示了MultiLayerNetwork.getLayers()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。MultiLayerNetwork.getLayers()方法的具体详情如下：
包路径：org.deeplearning4j.nn.multilayer.MultiLayerNetwork
类名称：MultiLayerNetwork
方法名：getLayers

MultiLayerNetwork.getLayers介绍

暂无

代码示例

代码示例来源：origin: deeplearning4j/dl4j-examples

Layer[] layers = net.getLayers();
long totalNumParams = 0;
for( int  i= 0; i < layers.length; i++) {

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * Get the output layer
 *
 * @return
 */
public Layer getOutputLayer() {
  return getLayers()[getLayers().length - 1];
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

@Override
protected Layer[] getOrderedLayers() {
  return network.getLayers();
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * Triggers the activation of the last hidden layer ie: not logistic regression
 *
 * @return the activation of the last hidden layer given the last input to the network
 */
public INDArray activate() {
  return getLayers()[getLayers().length - 1].activate();
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

public MultiLayerUpdater(MultiLayerNetwork network, INDArray updaterState) {
  super(network, updaterState);
  layersByName = new HashMap<>();
  Layer[] l = network.getLayers();
  for (int i = 0; i < l.length; i++) {
    layersByName.put(String.valueOf(i), l[i]);
  }
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * Return the weight matrices for a multi layer network
 * @param network the network to get the weights for
 * @return the weight matrices for a given multi layer network
 */
public static List<INDArray> weightMatrices(MultiLayerNetwork network) {
  List<INDArray> ret = new ArrayList<>();
  for (int i = 0; i < network.getLayers().length; i++) {
    ret.add(network.getLayers()[i].getParam(DefaultParamInitializer.WEIGHT_KEY));
  }
  return ret;
}

代码示例来源：origin: CampagneLaboratory/variationanalysis

private int getModelActivationNumber(MultiLayerNetwork model, FeatureMapper modelFeatureMapper) {
  int numActivations = 0;
  Layer[] layers = model.getLayers();
  INDArray inputFeatures = Nd4j.zeros(1, modelFeatureMapper.numberOfFeatures());
  int sum = model.feedForward(inputFeatures, false).stream().mapToInt(indArray ->
      indArray.data().asFloat().length).sum();
  System.out.println("Number of activations: " + sum);
  return sum;
}

代码示例来源：origin: CampagneLaboratory/variationanalysis

private int getModelActivationNumber(MultiLayerNetwork model, FeatureMapper modelFeatureMapper) {
  int numActivations = 0;
  Layer[] layers = model.getLayers();
  int totalNumParams = 0;
  INDArray inputFeatures = Nd4j.zeros(1, modelFeatureMapper.numberOfFeatures());
  int sum = model.feedForward(inputFeatures, false).stream().mapToInt(indArray ->
      indArray.data().asFloat().length).sum();
  System.out.println("Number of activations: " + sum);
  return sum;
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * Returns a 1 x m vector where the vector is composed of
 * a flattened vector of all of the weights for the
 * various neuralNets(w,hbias NOT VBIAS) and output layer
 *
 * @return the params for this neural net
 */
public INDArray params(boolean backwardOnly) {
  if (backwardOnly)
    return params();
  List<INDArray> params = new ArrayList<>();
  for (Layer layer : getLayers()) {
    INDArray layerParams = layer.params();
    if (layerParams != null)
      params.add(layerParams); //may be null: subsampling etc layers
  }
  return Nd4j.toFlattened('f', params);
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-modelimport

layersFromModel = ((MultiLayerNetwork) model).getLayers();
else
  layersFromModel = ((ComputationGraph) model).getLayers();

代码示例来源：origin: org.deeplearning4j/deeplearning4j-ui_2.11

layers = ((MultiLayerNetwork) model).getLayers();
} else if (model instanceof ComputationGraph) {
  layers = ((ComputationGraph) model).getLayers();

代码示例来源：origin: org.deeplearning4j/deeplearning4j-ui_2.10

layers = ((MultiLayerNetwork) model).getLayers();
} else if (model instanceof ComputationGraph) {
  layers = ((ComputationGraph) model).getLayers();

代码示例来源：origin: org.deeplearning4j/deeplearning4j-ui_2.10

if (model instanceof MultiLayerNetwork) {
  MultiLayerNetwork l = (MultiLayerNetwork) model;
  for (Layer layer : l.getLayers()) {
    if (!(layer instanceof FrozenLayer) && layer.type() == Layer.Type.CONVOLUTIONAL) {
      INDArray output = layer.activate();

代码示例来源：origin: org.deeplearning4j/deeplearning4j-ui_2.11

if (model instanceof MultiLayerNetwork) {
  MultiLayerNetwork l = (MultiLayerNetwork) model;
  for (Layer layer : l.getLayers()) {
    if (!(layer instanceof FrozenLayer) && layer.type() == Layer.Type.CONVOLUTIONAL) {
      INDArray output = layer.activate();

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

org.deeplearning4j.nn.api.Layer[] layers = editedModel.getLayers();
for (int i = frozenTill; i >= 0; i--) {

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

flattenedParams = params.dup();
int idx = 0;
for (int i = 0; i < getLayers().length; i++) {
  Layer layer = getLayer(i);
  int range = layer.numParams();

代码示例来源：origin: neo4j-contrib/neo4j-ml-procedures

List<Node> result = new ArrayList<>();
int layerCount = model.getnLayers();
for (Layer layer : model.getLayers()) {
  Node node = node("Layer",
      "type", layer.type().name(), "index", layer.getIndex(),

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

MultiLayerNetwork network = (MultiLayerNetwork) model;
try {
  if (network.getLayers() != null && network.getLayers().length > 0) {
    for (Layer layer : network.getLayers()) {
      if (layer instanceof RBM
              || layer instanceof org.deeplearning4j.nn.layers.feedforward.rbm.RBM) {

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * Clones the multilayernetwork
 * @return
 */
@Override
public MultiLayerNetwork clone() {
  MultiLayerConfiguration conf = this.layerWiseConfigurations.clone();
  MultiLayerNetwork ret = new MultiLayerNetwork(conf);
  ret.init(this.params().dup(), false);
  if (solver != null) {
    //If  solver is null: updater hasn't been initialized -> getUpdater call will force initialization, however
    Updater u = this.getUpdater();
    INDArray updaterState = u.getStateViewArray();
    if (updaterState != null) {
      ret.getUpdater().setStateViewArray(ret, updaterState.dup(), false);
    }
  }
  if (hasAFrozenLayer()) {
    //correct layers to frozen layers
    Layer[] clonedLayers = ret.getLayers();
    for (int i = 0; i < layers.length; i++) {
      if (layers[i] instanceof FrozenLayer) {
        clonedLayers[i] = new FrozenLayer(ret.getLayer(i));
      }
    }
    ret.setLayers(clonedLayers);
  }
  return ret;
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

private void initHelperMLN() {
  if (applyFrozen) {
    org.deeplearning4j.nn.api.Layer[] layers = origMLN.getLayers();
    for (int i = frozenTill; i >= 0; i--) {
      //unchecked?
      layers[i] = new FrozenLayer(layers[i]);
    }
    origMLN.setLayers(layers);
  }
  for (int i = 0; i < origMLN.getnLayers(); i++) {
    if (origMLN.getLayer(i) instanceof FrozenLayer) {
      frozenInputLayer = i;
    }
  }
  List<NeuralNetConfiguration> allConfs = new ArrayList<>();
  for (int i = frozenInputLayer + 1; i < origMLN.getnLayers(); i++) {
    allConfs.add(origMLN.getLayer(i).conf());
  }
  MultiLayerConfiguration c = origMLN.getLayerWiseConfigurations();
  unFrozenSubsetMLN = new MultiLayerNetwork(new MultiLayerConfiguration.Builder().backprop(c.isBackprop())
          .inputPreProcessors(c.getInputPreProcessors()).pretrain(c.isPretrain())
          .backpropType(c.getBackpropType()).tBPTTForwardLength(c.getTbpttFwdLength())
          .tBPTTBackwardLength(c.getTbpttBackLength()).confs(allConfs).build());
  unFrozenSubsetMLN.init();
  //copy over params
  for (int i = frozenInputLayer + 1; i < origMLN.getnLayers(); i++) {
    unFrozenSubsetMLN.getLayer(i - frozenInputLayer - 1).setParams(origMLN.getLayer(i).params());
  }
  //unFrozenSubsetMLN.setListeners(origMLN.getListeners());
}