[Spring boot] tensorflow 사용법

• tensorflow 사용방법 2가지 - depencency별 분류
  • docker build하여 사용할 시에 java - alpine 사용 불가
  1. tensorflow core platform dependency사용 방법
     
     

public float useModel(float[] fArr) {
        SavedModelBundle model = SavedModelBundle
                .load("model 경로", "serve");
        Session session = model.session();

        TFloat32 input = TFloat32.tensorOf(Shape.of(1, 2381), data -> data.set(NdArrays.vectorOf(fArr), 0));
        Tensor output = session.runner().feed("serving_default_input_1", input).fetch("StatefulPartitionedCall").run()
                .get(0);

        FloatDataBuffer fd = output.asRawTensor().data().asFloats();

        float result = fd.getFloat(0);
        System.out.println("반환 flaot : "+result); //0.93560594
        System.out.println(output.asRawTensor()); //DT_FLOAT tensor with shape [1, 1]
        System.out.println(output.dataType()); //DT_FLOAT
        System.out.println(output); //DenseTFloat32(shape=[1, 1])

				return result;
    }

 

 

<dependency> 
	<groupId>org.tensorflow</groupId>
    <artifactId>tensorflow-core-platform</artifactId>
    <version>0.4.2</version> 
</dependency>

 

2. tensorflow dependency사용방법

//model 사용하기
public void useModel(float[][] fArr) {
        SavedModelBundle model = SavedModelBundle.load(:"model 경로", "serve");
        Session session = model.session();
        Tensor input = Tensor.create(fArr);
        Tensor output = session.runner().feed("serving_default_input_1", input).fetch("StatefulPartitionedCall").run().get(0);
        Object result = output.copyTo(new float[1][1]);
        System.out.println(output);
        System.out.println(result);
    }

//모든함수 가져오기(호출명 등 확인)
public void getOperation() {
        try(SavedModelBundle model = SavedModelBundle.load("model 경로", "serve")){
            Graph graph = model.graph();
            Iterator<Operation> it = graph.operations();
            while(it.hasNext()) {
                Operation op = it.next();
                System.out.println(op.name());
            }
        }catch(Exception e) {
            System.out.println(e);
        }
    }

 

 

<dependency> 
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow</artifactId> 
<version>1.15.0</version>
</dependency>