用java写一个识别车辆的神经网络模型的代码

好的，以下是一个使用Java实现的识别车辆的神经网络模型的代码，基于deeplearning4j框架：

import org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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.multilayer.MultiLayerNetwork;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class VehicleDetector {

    public static void main(String[] args) throws Exception {
        // 加载CIFAR-10数据集
        DataSetIterator trainIter = new CifarDataSetIterator(10, 100, true);
        DataSetIterator testIter = new CifarDataSetIterator(10, 100, false);
        
        // 定义模型配置
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(12345)
                .l2(0.0005)
                .list()
                .layer(0, new ConvolutionLayer.Builder(5, 5)
                        .nIn(3)
                        .stride(1, 1)
                        .nOut(32)
                        .activation(Activation.RELU)
                        .build())
                .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
                        .kernelSize(2, 2)
                        .stride(2, 2)
                        .build())
                .layer(2, new ConvolutionLayer.Builder(5, 5)
                        .stride(1, 1)
                        .nOut(64)
                        .activation(Activation.RELU)
                        .build())
                .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
                        .kernelSize(2, 2)
                        .stride(2, 2)
                        .build())
                .layer(4, new DenseLayer.Builder().activation(Activation.RELU)
                        .nOut(1000).build())
                .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nOut(10)
                        .activation(Activation.SOFTMAX)
                        .build())
                .backprop(true)
                .pretrain(false).build();

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

        net.setListeners(new ScoreIterationListener(10));

        // 训练模型
        net.fit(trainIter);

        // 评估模型
        Evaluation eval = net.evaluate(testIter);
        System.out.println(eval.stats());
    }
}

以上代码中，我们首先使用`CifarDataSetIterator`加载CIFAR-10数据集，并定义了一个名为`conf`的神经网络配置。接着，我们使用`MultiLayerNetwork`类创建了一个多层神经网络模型`net`，并使用`fit`方法对模型进行训练。最后，我们使用`evaluate`方法评估模型性能。

请注意，以上代码仅供参考，实际应用中需要根据具体情况进行调整和优化。