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

Layer.type介绍

[英]Returns the layer type
[中]返回图层类型

代码示例

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

@Override
public Type type() {
  return insideLayer.type();
}

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

if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
  org.deeplearning4j.nn.conf.layers.ConvolutionLayer layer1 =
          (org.deeplearning4j.nn.conf.layers.ConvolutionLayer) layer.conf().getLayer();

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

if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
  org.deeplearning4j.nn.conf.layers.ConvolutionLayer layer1 =
          (org.deeplearning4j.nn.conf.layers.ConvolutionLayer) layer.conf().getLayer();

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

MultiLayerNetwork l = (MultiLayerNetwork) model;
for (Layer layer : l.getLayers()) {
  if (!(layer instanceof FrozenLayer) && layer.type() == Layer.Type.CONVOLUTIONAL) {
    INDArray output = layer.activate();
    int sampleDim = output.shape()[0] == 1 ? 0 : rnd.nextInt(output.shape()[0] - 1) + 1;
ComputationGraph l = (ComputationGraph) model;
for (Layer layer : l.getLayers()) {
  if (!(layer instanceof FrozenLayer) && layer.type() == Layer.Type.CONVOLUTIONAL) {
    INDArray output = layer.activate();
    int sampleDim = output.shape()[0] == 1 ? 0 : rnd.nextInt(output.shape()[0] - 1) + 1;

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

MultiLayerNetwork l = (MultiLayerNetwork) model;
for (Layer layer : l.getLayers()) {
  if (!(layer instanceof FrozenLayer) && layer.type() == Layer.Type.CONVOLUTIONAL) {
    INDArray output = layer.activate();
    int sampleDim = output.shape()[0] == 1 ? 0 : rnd.nextInt(output.shape()[0] - 1) + 1;
ComputationGraph l = (ComputationGraph) model;
for (Layer layer : l.getLayers()) {
  if (!(layer instanceof FrozenLayer) && layer.type() == Layer.Type.CONVOLUTIONAL) {
    INDArray output = layer.activate();
    int sampleDim = output.shape()[0] == 1 ? 0 : rnd.nextInt(output.shape()[0] - 1) + 1;

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

break;
if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
  task.setArchitectureType(Task.ArchitectureType.CONVOLUTION);
  break;
} else if (layer.type().equals(Layer.Type.RECURRENT)
        || layer.type().equals(Layer.Type.RECURSIVE)) {
  task.setArchitectureType(Task.ArchitectureType.RECURRENT);
  break;
  break;
if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
  task.setArchitectureType(Task.ArchitectureType.CONVOLUTION);
  break;
} else if (layer.type().equals(Layer.Type.RECURRENT)
        || layer.type().equals(Layer.Type.RECURSIVE)) {
  task.setArchitectureType(Task.ArchitectureType.RECURRENT);
  break;

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

for (Layer layer : model.getLayers()) {
  Node node = node("Layer",
      "type", layer.type().name(), "index", layer.getIndex(),
      "pretrainLayer", layer.isPretrainLayer(), "miniBatchSize", layer.getInputMiniBatchSize(),
      "numParams", layer.numParams());

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

if (inputIs2d && input.rank() == 3 && layers[layers.length - 1].type() == Type.RECURRENT) {