本文整理了Java中org.deeplearning4j.nn.api.Layer.paramTable()方法的一些代码示例，展示了Layer.paramTable()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。Layer.paramTable()方法的具体详情如下：
包路径：org.deeplearning4j.nn.api.Layer
类名称：Layer
方法名：paramTable

Layer.paramTable介绍

暂无

代码示例

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

@Override
public Map<String, INDArray> paramTable(boolean backpropParamsOnly) {
  return insideLayer.paramTable(backpropParamsOnly);
}

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

@Override
public Map<String, INDArray> paramTable() {
  return insideLayer.paramTable();
}

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

public Map<String, INDArray> paramTable(boolean backpropParamsOnly) {
  //Get all parameters from all layers
  Map<String, INDArray> allParams = new LinkedHashMap<>();
  for (int i = 0; i < layers.length; i++) {
    Map<String, INDArray> paramMap = layers[i].paramTable(backpropParamsOnly);
    for (Map.Entry<String, INDArray> entry : paramMap.entrySet()) {
      String newKey = i + "_" + entry.getKey();
      allParams.put(newKey, entry.getValue());
    }
  }
  return allParams;
}

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

public Map<String, INDArray> paramTable(boolean backpropParamsOnly) {
  //Get all parameters from all layers
  Map<String, INDArray> allParams = new LinkedHashMap<>();
  for (Layer layer : layers) {
    Map<String, INDArray> paramMap = layer.paramTable(backpropParamsOnly);
    for (Map.Entry<String, INDArray> entry : paramMap.entrySet()) {
      String newKey = layer.conf().getLayer().getLayerName() + "_" + entry.getKey();
      allParams.put(newKey, entry.getValue());
    }
  }
  return allParams;
}

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

public FrozenLayer(Layer insideLayer) {
  this.insideLayer = insideLayer;
  if (insideLayer instanceof OutputLayer) {
    throw new IllegalArgumentException("Output Layers are not allowed to be frozen " + layerId());
  }
  this.insideLayer = insideLayer;
  this.zeroGradient = new DefaultGradient(insideLayer.params());
  if (insideLayer.paramTable() != null) {
    for (String paramType : insideLayer.paramTable().keySet()) {
      //save memory??
      zeroGradient.setGradientFor(paramType, null);
    }
  }
}

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

/**
 * Copy Keras layer weights to DL4J Layer.
 *
 * @param layer
 * @throws InvalidKerasConfigurationException
 */
public void copyWeightsToLayer(org.deeplearning4j.nn.api.Layer layer) throws InvalidKerasConfigurationException {
  if (this.getNumParams() > 0) {
    String dl4jLayerName = layer.conf().getLayer().getLayerName();
    String kerasLayerName = this.getLayerName();
    String msg = "Error when attempting to copy weights from Keras layer " + kerasLayerName + " to DL4J layer "
            + dl4jLayerName;
    if (this.weights == null)
      throw new InvalidKerasConfigurationException(msg + "(weights is null)");
    Set<String> paramsInLayer = new HashSet<String>(layer.paramTable().keySet());
    Set<String> paramsInKerasLayer = new HashSet<String>(this.weights.keySet());
    /* Check for parameters in layer for which we don't have weights. */
    paramsInLayer.removeAll(paramsInKerasLayer);
    for (String paramName : paramsInLayer)
      throw new InvalidKerasConfigurationException(
              msg + "(no stored weights for parameter " + paramName + ")");
    /* Check for parameters NOT in layer for which we DO have weights. */
    paramsInKerasLayer.removeAll(layer.paramTable().keySet());
    for (String paramName : paramsInKerasLayer)
      throw new InvalidKerasConfigurationException(msg + "(found no parameter named " + paramName + ")");
    /* Copy weights. */
    for (String paramName : layer.paramTable().keySet())
      layer.setParam(paramName, this.weights.get(paramName));
  }
}

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

Set<String> paraNames = currentLayer.conf().getLearningRateByParam().keySet();
for (String aP : paraNames) {
  String paramS = ArrayUtils.toString(currentLayer.paramTable().get(aP).shape());
  paramShape += aP + ":" + paramS + ", ";

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

Set<String> paraNames = currentLayer.conf().getLearningRateByParam().keySet();
for (String aP : paraNames) {
  String paramS = ArrayUtils.toString(currentLayer.paramTable().get(aP).shape());
  paramShape += aP + ":" + paramS + ", ";

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

Map<String, INDArray> paramTable = layer.paramTable();
List<String> paramNames = new ArrayList<>(paramTable.keySet());
int[] paramEnds = new int[paramNames.size()];

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

int currentUpdaterOffset = 0;
for (int i = 0; i < layers.length; i++) {
  Map<String, INDArray> layerParamTable = layers[i].paramTable();
  if (layerParamTable != null) {
    List<String> variables = new ArrayList<>(layerParamTable.keySet()); //Is from a set, but iteration order should be fixed per layer as it's a from a LinkedHashSet

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

NeuralNetConfiguration conf = l.conf();
Map<String, Double> layerLrs = conf.getLearningRateByParam();
Set<String> backpropParams = l.paramTable(true).keySet();
for (Map.Entry<String, Double> entry : layerLrs.entrySet()) {
  if (!backpropParams.contains(entry.getKey()))
Map<String, Double> layerLrs = conf.getLearningRateByParam();
String layerName = conf.getLayer().getLayerName();
Set<String> backpropParams = l.paramTable(true).keySet();
for (Map.Entry<String, Double> entry : layerLrs.entrySet()) {
  if (!backpropParams.contains(entry.getKey()))