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Java接入通义千问简单方法示例
1. 导入依赖
在Maven项目中,添加如下依赖:
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-nn</artifactId>
<version>1.0.0-beta7</version>
</dependency>
2. 数据准备
加载训练数据,确保数据格式符合模型要求。例如,加载MNIST数据集:
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
int batchSize = 64;
MnistDataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
MnistDataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, 12345);
3. 模型构建
构建神经网络模型,指定层结构、激活函数、损失函数和优化器。例如,一个简单的多层感知机(MLP):
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
int numInputs = 784;
int numOutputs = 10;
int numHiddenNodes = 100;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(Updater.ADAM)
.l2(0.001)
.list()
.layer(0, new DenseLayer.Builder()
.nIn(numInputs)
.nOut(numHiddenNodes)
.weightInit(WeightInit.XAVIER)
.activation("relu")
.build())
.layer(1, new OutputLayer.Builder()
.nIn(numHiddenNodes)
.nOut(numOutputs)
.weightInit(WeightInit.XAVIER)
.activation("softmax")
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.build())
.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
4. 模型训练
使用准备好的数据对模型进行训练。训练过程需要迭代训练数据,更新模型权重。
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
int epochs = 10;
model.addListeners(new ScoreIterationListener(100));
for (int i = 0; i < epochs; i++) {
mnistTrain.reset();
while (mnistTrain.hasNext()) {
model.fit(mnistTrain.next());
}
}
5. 模型评估
训练完成后,使用测试数据评估模型性能。例如,计算分类准确率:
mnistTest.reset();
double accuracy = 0;
while (mnistTest.hasNext()) {
INDArray test = mnistTest.next().getFeatureMatrix();
INDArray predicted = model.output(test);
int[] predictedLabels = predicted.argMax(1).toIntVector();
int[] actualLabels = mnistTest.next().getLabels().toIntVector();
for (int j = 0; j < predictedLabels.length; j++) {
if (predictedLabels[j] == actualLabels[j]) {
accuracy++;
}
}
}
accuracy /= mnistTest.totalExamples();
System.out.println("Model accuracy: " + accuracy);
6. 模型保存和加载
完成训练后,可以将模型保存到文件中,以便以后使用:
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import java.io.File;
File locationToSave = new File("savedModel.zip");
NeuralNetConfiguration netConfig = model.getLayerWiseConfigurations();
ModelSerializer.writeModel(model, locationToSave, true);
要加载保存的模型:
MultiLayerNetwork loadedModel = ModelSerializer.restoreMultiLayerNetwork(locationToSave);
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