如何在DeepLearning4J中添加具有指定值的激活层？

import org.deeplearning4j.nn.conf.layers.ActivationLayer;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.nd4j.linalg.activations.Activation;

NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder();

int numInputs = 10; // 输入层的节点数
int numHiddenNodes = 20; // 隐藏层的节点数

// 输入层
Layer inputLayer = new DenseLayer.Builder()
    .nIn(numInputs)
    .nOut(numHiddenNodes)
    .activation(Activation.IDENTITY) // 输入层的激活函数为恒等函数
    .build();

// 隐藏层
Layer hiddenLayer = new ActivationLayer.Builder()
    .activation(Activation.TANH) // 隐藏层的激活函数为双曲正切函数
    .build();

int numOutputs = 2; // 输出层的节点数

Layer outputLayer = new ActivationLayer.Builder()
    .activation(Activation.SOFTMAX) // 输出层的激活函数为Softmax函数
    .nIn(numHiddenNodes)
    .nOut(numOutputs)
    .build();

MultiLayerConfiguration conf = builder
    .list()
    .layer(0, inputLayer)
    .layer(1, hiddenLayer)
    .layer(2, outputLayer)
    .build();

MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();