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

Layer.activate介绍

[英]Trigger an activation with the last specified input
[中]使用最后指定的输入触发激活

代码示例

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

@Override
public INDArray activate() {
  return insideLayer.activate();
}

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

@Override
public INDArray activate(INDArray input) {
  return insideLayer.activate(input);
}

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

/**
 * Triggers the activation for a given layer
 *
 * @param layer the layer to activate on
 * @return the activation for a given layer
 */
public INDArray activate(int layer) {
  return getLayer(layer).activate();
}

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

@Override
public INDArray activate(INDArray input, TrainingMode training) {
  logTestMode(training);
  return insideLayer.activate(input, TrainingMode.TEST);
}

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

@Override
public INDArray activate(boolean training) {
  logTestMode(training);
  return insideLayer.activate(false);
}

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

@Override
public INDArray activate(INDArray input, boolean training) {
  logTestMode(training);
  return insideLayer.activate(input, false);
}

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

/**
 * Triggers the activation of the given layer
 *
 * @param layer the layer to trigger on
 * @param input the input to the hidden layer
 * @return the activation of the layer based on the input
 */
public INDArray activate(int layer, INDArray input) {
  return getLayer(layer).activate(input);
}

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

@Override
public INDArray doForward(boolean training) {
  if (!canDoForward())
    throw new IllegalStateException("Cannot do forward pass: all inputs not set");
  return layer.activate(training);
}

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

@Override
public INDArray activate(TrainingMode training) {
  logTestMode(training);
  return insideLayer.activate(TrainingMode.TEST);
}

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

currInput = temp.get(temp.size() - 1);
} else {
  currInput = layers[i].activate(currInput, training);

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

/**
 * Calculate activation from previous layer including pre processing where necessary
 *
 * @param curr  the current layer
 * @param input the input
 * @return the activation from the previous layer
 */
public INDArray activationFromPrevLayer(int curr, INDArray input, boolean training) {
  if (getLayerWiseConfigurations().getInputPreProcess(curr) != null)
    input = getLayerWiseConfigurations().getInputPreProcess(curr).preProcess(input, getInputMiniBatchSize());
  INDArray ret = layers[curr].activate(input, training);
  return ret;
}

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

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;
    if (cnt == 0) {
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;
    if (cnt == 0) {

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

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;
    if (cnt == 0) {
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;
    if (cnt == 0) {

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

@Override
public INDArray preOutput(INDArray x) {
  INDArray lastLayerActivation = x;
  for (int i = 0; i < layers.length - 1; i++) {
    if (getLayerWiseConfigurations().getInputPreProcess(i) != null)
      lastLayerActivation = getLayerWiseConfigurations().getInputPreProcess(i).preProcess(lastLayerActivation,
              getInputMiniBatchSize());
    lastLayerActivation = layers[i].activate(lastLayerActivation);
  }
  if (getLayerWiseConfigurations().getInputPreProcess(layers.length - 1) != null)
    lastLayerActivation = getLayerWiseConfigurations().getInputPreProcess(layers.length - 1)
            .preProcess(lastLayerActivation, getInputMiniBatchSize());
  return layers[layers.length - 1].preOutput(lastLayerActivation);
}

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

input = ((MultiLayerNetwork) layers[i]).rnnTimeStep(input);
} else {
  input = layers[i].activate(input, false);