Java TensorFlow类代码示例

import org.tensorflow.TensorFlow; //导入依赖的package包/类
private void loadGraph(InputStream is, Graph g) throws IOException {
  final long startMs = System.currentTimeMillis();

  Trace.beginSection("initializeTensorFlow");

  Trace.beginSection("readGraphDef");
  // TODO(ashankar): Can we somehow mmap the contents instead of copying them?
  byte[] graphDef = new byte[is.available()];
  final int numBytesRead = is.read(graphDef);
  if (numBytesRead != graphDef.length) {
    throw new IOException(
        "read error: read only "
            + numBytesRead
            + " of the graph, expected to read "
            + graphDef.length);
  }
  Trace.endSection();

  Trace.beginSection("importGraphDef");
  try {
    g.importGraphDef(graphDef);
  } catch (IllegalArgumentException e) {
    throw new IOException("Not a valid TensorFlow Graph serialization: " + e.getMessage());
  }
  Trace.endSection();

  Trace.endSection(); // initializeTensorFlow.

  final long endMs = System.currentTimeMillis();
  Log.i(
      TAG,
      "Model load took " + (endMs - startMs) + "ms, TensorFlow version: " + TensorFlow.version());
}
 

import org.tensorflow.TensorFlow; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
    try (Graph g = new Graph()) {
        final String value = "Hello from " + TensorFlow.version();

        // Construct the computation graph with a single operation, a constant
        // named "MyConst" with a value "value".
        try (Tensor t = Tensor.create(value.getBytes("UTF-8"))) {
            // The Java API doesn't yet include convenience functions for adding operations.
            g.opBuilder("Const", "MyConst").setAttr("dtype", t.dataType()).setAttr("value", t).build();
        }

        // Execute the "MyConst" operation in a Session.
        try (Session s = new Session(g);
             Tensor output = s.runner().fetch("MyConst").run().get(0)) {
            System.out.println(new String(output.bytesValue(), "UTF-8"));
        }
    }
}
 

import org.tensorflow.TensorFlow; //导入依赖的package包/类
@Test
public void testTf() throws UnsupportedEncodingException {
    try (Graph g = new Graph()) {
        final String value = "Hello from " + TensorFlow.version();

        // Construct the computation graph with a single operation, a constant
        // named "MyConst" with a value "value".
        try (Tensor t = Tensor.create(value.getBytes("UTF-8"))) {
            // The Java API doesn't yet include convenience functions for adding operations.
            g.opBuilder("Const", "MyConst").setAttr("dtype", t.dataType()).setAttr("value", t).build();
        }

        // Execute the "MyConst" operation in a Session.
        try (Session s = new Session(g); Tensor output = s.runner().fetch("MyConst").run().get(0)) {
            logger.info(new String(output.bytesValue(), "UTF-8"));
            logger.info(output.toString());
        }
    }
}
 

import org.tensorflow.TensorFlow; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
    try (Graph g = new Graph()) {
        final String value = "Hello from " + TensorFlow.version();

        // Construct the computation graph with a single operation, a constant
        // named "MyConst" with a value "value".
        try (Tensor t = Tensor.create(value.getBytes("UTF-8"))) {
            // The Java API doesn't yet include convenience functions for adding operations.
            g.opBuilder("Const", "MyConst").setAttr("dtype", t.dataType()).setAttr("value", t).build();
        }

        // Execute the "MyConst" operation in a Session.
        try (Session s = new Session(g);
                Tensor output = s.runner().fetch("MyConst").run().get(0)) {
            System.out.println(new String(output.bytesValue(), "UTF-8"));
        }
    }
}
 

import org.tensorflow.TensorFlow; //导入依赖的package包/类
private static void printUsage(PrintStream s) {
    final String url
            = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip";
    s.println(
            "Java program that uses a pre-trained Inception model (http://arxiv.org/abs/1512.00567)");
    s.println("to label JPEG images.");
    s.println("TensorFlow version: " + TensorFlow.version());
    s.println();
    s.println("Usage: label_image <model dir> <image file>");
    s.println();
    s.println("Where:");
    s.println("<model dir> is a directory containing the unzipped contents of the inception model");
    s.println("            (from " + url + ")");
    s.println("<image file> is the path to a JPEG image file");
}
 

import org.tensorflow.TensorFlow; //导入依赖的package包/类
private static void printUsage(PrintStream s) {
  final String url =
      "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip";
  s.println(
      "Java program that uses a pre-trained Inception model (http://arxiv.org/abs/1512.00567)");
  s.println("to label JPEG images.");
  s.println("TensorFlow version: " + TensorFlow.version());
  s.println();
  s.println("Usage: label_image <model dir> <image file>");
  s.println();
  s.println("Where:");
  s.println("<model dir> is a directory containing the unzipped contents of the inception model");
  s.println("            (from " + url + ")");
  s.println("<image file> is the path to a JPEG image file");
}
 

import org.tensorflow.TensorFlow; //导入依赖的package包/类
public static void main(String[] args) {
  System.out.println("TensorFlowExample using TensorFlow version: " +  TensorFlow.version());
}