深度学习4J LSTM网络的输入数据模型

深度学习4J（DL4J）是一个用于构建深度学习模型的Java库。LSTM（长短期记忆）是一种特殊的循环神经网络（Recurrent Neural Network，RNN）架构，用于处理序列数据。下面是一个示例，展示了如何在DL4J中构建LSTM网络的输入数据模型。

首先，你需要导入所需的DL4J库：

import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.BackpropType;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor;
import org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor;
import org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.vertex.GraphVertex;
import org.deeplearning4j.nn.graph.vertex.impl.LSTMVertex;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.schedule.MapSchedule;
import org.nd4j.linalg.schedule.ScheduleType;
import org.nd4j.linalg.schedule.Scheduler;
import org.nd4j.linalg.schedule.StepSchedule;

然后，你可以使用以下代码创建一个LSTM网络的输入数据模型：

// 定义LSTM网络的输入数据模型
public ComputationGraph createLSTMModel() {
    int numInputs = 10; // 输入特征的数量
    int numOutputs = 2; // 输出类别的数量
    int hiddenSize = 20; // LSTM隐藏层的大小
    
    // 构建神经网络的配置
    NeuralNetConfiguration.Builder configBuilder = new NeuralNetConfiguration.Builder();
    configBuilder.seed(123);
    configBuilder.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer);
    configBuilder.gradientNormalizationThreshold(1.0);
    
    // 创建LSTM层
    LSTM.Builder lstmBuilder = new LSTM.Builder();
    lstmBuilder.nIn(numInputs);
    lstmBuilder.nOut(hiddenSize);
    lstmBuilder.activation(Activation.TANH);
    
    // 创建输出层
    OutputLayer.Builder outputBuilder = new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT);
    outputBuilder.nIn(hiddenSize);
    outputBuilder.nOut(numOutputs);
    outputBuilder.activation(Activation.SOFTMAX);
    
    // 创建输入/输出层
    InputLayer inputLayer = new InputLayer.Builder().name("input").nIn(numInputs).nOut(numInputs).build();
    FeedForwardLayer outputLayer = outputBuilder.build();
    
    // 创建网络
    ComputationGraphConfiguration.GraphBuilder graphBuilder = new NeuralNetConfiguration.Builder().graphBuilder();
    graphBuilder.addInputs("input").addLayer("lstm", lstmBuilder.build(), "input").addLayer("output", outputLayer, "lstm");
    graphBuilder.setOutputs("output");
    
    // 构建计算图
    ComputationGraph graph = new ComputationGraph(graphBuilder.build());
    graph.init();
    
    return graph;
}

以上代码创建了一个包含一个LSTM层和一个输出层的计算图。你可以根据实际需求修改网络的配置和参数。

希望这个示例能够帮助到你构建深度学习4J中LSTM网络的输入数据模型。