Java o=savedModel.metaGraphDef（）.getSignatureDefMap（）.get（“默认服务”）

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o=savedModel.metaGraphDef（）.getSignatureDefMap（）.get（“默认服务”） try (SavedModelBundle model = SavedModelBundle.load("./house_price_median_income", "serve")) { try (Session session = model.session()) { Session.Runner runner = session.runner(); float[][] in = new float[][]{ {2.1518f} } ; Tensor<?> jack = Tensor.create(in); runner.feed("serving_default_layer1_input", jack); float[][] probabilities = runner.fetch("StatefulPartitionedCall").run().get(0).copyTo(new float[1][1]); for (int i = 0; i < probabilities.length; ++i) { System.out.println(String.format("-- Input #%d", i)); for (int j = 0; j < probabilities[i].length; ++j) { System.out.println(String.format("Class %d - %f", i, probabilities[i][j])); } } } }

 Graph graph = model.graph();
    Iterator<Operation> itr = graph.operations();
    while (itr.hasNext()) {
        GraphOperation e = (GraphOperation)itr.next();
        System.out.println(e);

import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.exceptions.TensorFlowException;
import org.tensorflow.Session.Run;
import org.tensorflow.Graph;
import org.tensorflow.Operation;
import org.tensorflow.Output;
import org.tensorflow.GraphOperation;
import org.tensorflow.proto.framework.SignatureDef;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import org.tensorflow.proto.framework.MetaGraphDef;
import java.util.Map;
import org.tensorflow.proto.framework.TensorInfo;
import org.tensorflow.types.TFloat32;
import org.tensorflow.tools.Shape;
import java.nio.FloatBuffer;
import org.tensorflow.tools.buffer.DataBuffers;
import org.tensorflow.tools.ndarray.FloatNdArray;
import org.tensorflow.tools.ndarray.StdArrays;
import org.tensorflow.proto.framework.TensorInfo;

public class v2tensor {
    public static void main(String[] args) {
     try (SavedModelBundle savedModel = SavedModelBundle.load("./house_price_median_income", "serve")) {
        SignatureDef modelInfo = savedModel.metaGraphDef().getSignatureDefMap().get("serving_default");
        TensorInfo input1 = null;
        TensorInfo output1 = null;
        Map<String, TensorInfo> inputs = modelInfo.getInputsMap();
        for(Map.Entry<String, TensorInfo> input : inputs.entrySet()) {
            if (input1 == null) {
                input1 = input.getValue();
                System.out.println(input1.getName());
            }
            System.out.println(input);
        }
        Map<String, TensorInfo> outputs = modelInfo.getOutputsMap();
        for(Map.Entry<String, TensorInfo> output : outputs.entrySet()) {
            if (output1 == null) {
                output1=output.getValue();
            }
            System.out.println(output);
        }

        try (Session session = savedModel.session()) {
            Session.Runner runner = session.runner();
            FloatNdArray matrix = StdArrays.ndCopyOf(new float[][]{ { 2.1518f } } );

            try (Tensor<TFloat32> jack = TFloat32.tensorOf(matrix) ) {
                runner.feed(input1.getName(), jack);
                try ( Tensor<TFloat32> rezz = runner.fetch(output1.getName()).run().get(0).expect(TFloat32.DTYPE) ) { 
                    TFloat32 data = rezz.data();
                    data.scalars().forEachIndexed((i, s) -> {
                        System.out.println(s.getFloat());
                    }   );
                }
            }
        }
    } catch (TensorFlowException ex) {
        ex.printStackTrace();   
    }
    }
}