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Showing content from https://deeplearning4j.konduit.ai/nd4j/how-to-guides/other-framework-interop/tensorflow below:

Tensorflow | Deeplearning4j

Tensorflow | Deeplearning4j
  1. Nd4j
  2. How To Guides
  3. Other Framework Interop
Tensorflow

Tensorflow interop Key features and brief samples.

Nd4j allows execution of models via javacpp's tensorflow bindings using nd4j's INDArray as a data structure. Leveraging the nd4j-tensorflow interop is fairly simple.

Note, this is based on tensorflow 1.x. TF java 2 will be coming at a later time.

A tensorflow model can be loaded and executed as follows:

        File f = new File ("path/to/your/model.pb");
        INDArray x = Nd4j.scalar(1.0f).reshape(1,1);
        INDArray y = Nd4j.scalar(1.0f).reshape(1,1);
        List<String> inputNames = Arrays.asList("x","y");
        List<String> outputNames = Arrays.asList("z");
        GraphRunner tensorflowRunner = GraphRunner.builder()
                .inputNames(inputNames)
                .graphPath(f)
                .outputNames(outputNames).
                build();
        Map<String,INDArray> inputs = new LinkedHashMap<>();
        inputs.put("x",x);
        inputs.put("y",y);
        Map<String, INDArray> exec = tensorflowRunner.exec(inputs);
        INDArray z = exec.get("z");

This outputs a result with a map of output names to the ndarray result from tensorflow.

In maven, add the following dependency:

<dependency>
   <groupId>org.nd4j</groupId>
   <artifactId>nd4j-tensorflow</groupId>
   <version>${nd4j.version}</version>
</dependency>

Note, this depends on javacpp's tensorflow bindings. This means tensorflow's native binaries are managed by javacpp. Javacpp will bundle all native binaries for all platforms by default unless you specify a platform. You can do this by specifying a -Dplatform=your-platform-of-choice. You may find more here in the javacpp docs.


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