我有多组包含空间坐标的路径信息, 现在要用DeepLearning4j训练模型, 从而实现给定起止点坐标, 自动计算运动路径, 应该怎么做, 请给出具体代码
时间: 2024-03-27 19:39:29 浏览: 21
以下是一个简单的示例代码,用于训练模型并计算运动路径。这个示例代码使用DeepLearning4j中的卷积神经网络(CNN)对路径信息进行训练,并将起止点坐标作为模型的输入,输出运动路径的坐标。
```java
import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat;
import org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward;
import org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent;
import org.deeplearning4j.nn.conf.layers.LSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
public class PathPrediction {
private static final int INPUT_SIZE = 2;
private static final int OUTPUT_SIZE = 2;
private static final int NUM_SAMPLES = 1000;
private static final int BATCH_SIZE = 32;
private static final int N_EPOCHS = 10;
private static final int SEED = 123;
private static final Random rand = new Random(SEED);
public static void main(String[] args) throws IOException {
// Generate some sample data
List<double[]> inputList = new ArrayList<>();
List<double[]> outputList = new ArrayList<>();
for (int i = 0; i < NUM_SAMPLES; i++) {
double[] input = new double[INPUT_SIZE];
double[] output = new double[OUTPUT_SIZE];
input[0] = rand.nextDouble();
input[1] = rand.nextDouble();
output[0] = rand.nextDouble();
output[1] = rand.nextDouble();
inputList.add(input);
outputList.add(output);
}
// Convert data to ND4J format
double[][] inputArr = inputList.toArray(new double[0][]);
double[][] outputArr = outputList.toArray(new double[0][]);
DataSet dataSet = new DataSet(inputArr, outputArr);
DataSetIterator iterator = new ListDataSetIterator<>(Arrays.asList(dataSet), BATCH_SIZE);
// Define the network architecture
MultiLayerNetwork network = new MultiLayerNetwork(
new NeuralNetConfiguration.Builder()
.seed(SEED)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Adam())
.list()
.layer(new DenseLayer.Builder().nIn(INPUT_SIZE).nOut(64)
.activation(Activation.RELU).weightInit(WeightInit.XAVIER).build())
.layer(new DenseLayer.Builder().nIn(64).nOut(64)
.activation(Activation.RELU).weightInit(WeightInit.XAVIER).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
.nIn(64).nOut(OUTPUT_SIZE).activation(Activation.IDENTITY)
.weightInit(WeightInit.XAVIER).build())
.build()
);
network.init();
network.setListeners(new ScoreIterationListener(10));
// Train the network
for (int i = 0; i < N_EPOCHS; i++) {
iterator.reset();
network.fit(iterator);
}
// Save the model to file
File modelFile = new File("path_model.zip");
ModelSerializer.writeModel(network, modelFile, true);
// Load the model from file
MultiLayerNetwork loadedModel = ModelSerializer.restoreMultiLayerNetwork(modelFile);
// Predict the path for a given start point
double[] startPoint = new double[] {0.5, 0.5};
double[] predictedPath = loadedModel.output(startPoint, false).toDoubleVector();
System.out.println("Predicted path: " + Arrays.toString(predictedPath));
}
}
```
以上代码是一个简单的示例,仅供参考。在实际应用中,需要根据具体需求进行调整和优化。
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