Comment on page
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.
Last modified 1yr ago