org.tensorflow.Graph.<init>()方法的使用及代码示例
本文整理了Java中org.tensorflow.Graph.<init>()方法的一些代码示例，展示了Graph.<init>()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Graph.<init>()方法的具体详情如下：
包路径：org.tensorflow.Graph
类名称：Graph
方法名：<init>
Graph.<init>介绍
[英]Create an empty Graph.
[中]创建一个空图形。
代码示例
代码示例来源：origin: apache/ignite
/** {@inheritDoc} */
@Override public Session parseModel(byte[] mdl) {
Graph graph = new Graph();
graph.importGraphDef(mdl);
return new Session(graph);
}
}代码示例来源：origin: org.springframework.cloud.stream.app/spring-cloud-starter-stream-common-tensorflow
public TensorFlowService(Resource modelLocation) throws IOException {
try (InputStream is = modelLocation.getInputStream()) {
if (logger.isInfoEnabled()) {
logger.info("Loading TensorFlow graph model: " + modelLocation);
}
graph = new Graph();
graph.importGraphDef(StreamUtils.copyToByteArray(is));
}
}代码示例来源：origin: org.tensorflow/libtensorflow
/**
* Create a SavedModelBundle object from a handle to the C TF_Graph object and to the C TF_Session
* object, plus the serialized MetaGraphDef.
*
* <p>Invoked from the native load method. Takes ownership of the handles.
*/
private static SavedModelBundle fromHandle(
long graphHandle, long sessionHandle, byte[] metaGraphDef) {
Graph graph = new Graph(graphHandle);
Session session = new Session(graph, sessionHandle);
return new SavedModelBundle(graph, session, metaGraphDef);
}代码示例来源：origin: org.bytedeco.javacpp-presets/tensorflow
/**
* Create a SavedModelBundle object from a handle to the C TF_Graph object and to the C TF_Session
* object, plus the serialized MetaGraphDef.
*
* <p>Invoked from the native load method. Takes ownership of the handles.
*/
private static SavedModelBundle fromHandle(
long graphHandle, long sessionHandle, byte[] metaGraphDef) {
Graph graph = new Graph(graphHandle);
Session session = new Session(graph, sessionHandle);
return new SavedModelBundle(graph, session, metaGraphDef);
}代码示例来源：origin: spring-cloud-stream-app-starters/tensorflow
public TensorFlowService(Resource modelLocation) {
if (logger.isInfoEnabled()) {
logger.info("Loading TensorFlow graph model: " + modelLocation);
}
graph = new Graph();
byte[] model = new ModelExtractor().getModel(modelLocation);
graph.importGraphDef(model);
}代码示例来源：origin: org.bytedeco.javacpp-presets/tensorflow
public TensorFlowInferenceInterface(InputStream is) {
prepareNativeRuntime();
// modelName is redundant for model loading from input stream, here is for
// avoiding error in initialization as modelName is marked final.
this.modelName = "";
this.g = new Graph();
this.sess = new Session(g);
this.runner = sess.runner();
try {
if (VERSION.SDK_INT >= 18) {
}
int baosInitSize = is.available() > 16384 ? is.available() : 16384;
ByteArrayOutputStream baos = new ByteArrayOutputStream(baosInitSize);
int numBytesRead;
byte[] buf = new byte[16384];
while ((numBytesRead = is.read(buf, 0, buf.length)) != -1) {
baos.write(buf, 0, numBytesRead);
}
byte[] graphDef = baos.toByteArray();
if (VERSION.SDK_INT >= 18) {
}
loadGraph(graphDef, g);
Log.i(TAG, "Successfully loaded model from the input stream");
if (VERSION.SDK_INT >= 18) {
}
} catch (IOException e) {
throw new RuntimeException("Failed to load model from the input stream", e);
}
}代码示例来源：origin: spotify/zoltar
/**
* Note: Please use Models from zoltar-models module.
*
* <p>Creates a TensorFlow model based on a frozen, serialized TensorFlow {@link Graph}.
*
* @param id model id @{link Model.Id}.
* @param graphDef byte array representing the TensorFlow {@link Graph} definition.
* @param config ConfigProto config for TensorFlow {@link Session}.
* @param prefix a prefix that will be prepended to names in graphDef.
*/
public static TensorFlowGraphModel create(
final Model.Id id,
final byte[] graphDef,
@Nullable final ConfigProto config,
@Nullable final String prefix)
throws IOException {
final Graph graph = new Graph();
final Session session = new Session(graph, config != null ? config.toByteArray() : null);
final long loadStart = System.currentTimeMillis();
if (prefix == null) {
LOG.debug("Loading graph definition without prefix");
graph.importGraphDef(graphDef);
} else {
LOG.debug("Loading graph definition with prefix: {}", prefix);
graph.importGraphDef(graphDef, prefix);
}
LOG.info("TensorFlow graph loaded in {} ms", System.currentTimeMillis() - loadStart);
return new AutoValue_TensorFlowGraphModel(id, graph, session);
}代码示例来源：origin: org.bytedeco.javacpp-presets/tensorflow
this.g = new Graph();
this.sess = new Session(g);
this.runner = sess.runner();代码示例来源：origin: com.spotify/zoltar-tensorflow
/**
* Note: Please use Models from zoltar-models module.
*
* <p>Creates a TensorFlow model based on a frozen, serialized TensorFlow {@link Graph}.</p>
*
* @param id model id @{link Model.Id}.
* @param graphDef byte array representing the TensorFlow {@link Graph} definition.
* @param config ConfigProto config for TensorFlow {@link Session}.
* @param prefix a prefix that will be prepended to names in graphDef.
*/
public static TensorFlowGraphModel create(final Model.Id id,
final byte[] graphDef,
@Nullable final ConfigProto config,
@Nullable final String prefix)
throws IOException {
final Graph graph = new Graph();
final Session session = new Session(graph, config != null ? config.toByteArray() : null);
final long loadStart = System.currentTimeMillis();
if (prefix == null) {
LOG.debug("Loading graph definition without prefix");
graph.importGraphDef(graphDef);
} else {
LOG.debug("Loading graph definition with prefix: {}", prefix);
graph.importGraphDef(graphDef, prefix);
}
LOG.info("TensorFlow graph loaded in {} ms", System.currentTimeMillis() - loadStart);
return new AutoValue_TensorFlowGraphModel(id, graph, session);
}代码示例来源：origin: jdye64/nifi-addons
private float[] executeInceptionGraph(byte[] graphDef, Tensor image, String feedNodeName, String outputNodeName) {
try (Graph g = new Graph()) {
g.importGraphDef(graphDef);
try (Session s = new Session(g)) {
Tensor result = s.runner().feed(feedNodeName, image).fetch(outputNodeName).run().get(0);
final long[] rshape = result.shape();
if (result.numDimensions() != 2 || rshape[0] != 1) {
throw new RuntimeException(
String.format(
"Expected model to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape %s",
Arrays.toString(rshape)));
}
int nlabels = (int) rshape[1];
return result.copyTo(new float[1][nlabels])[0];
}
}
}代码示例来源：origin: tahaemara/object-recognition-tensorflow
private static float[] executeInceptionGraph(byte[] graphDef, Tensor image) {
try (Graph g = new Graph()) {
g.importGraphDef(graphDef);
try (Session s = new Session(g);
Tensor result = s.runner().feed("DecodeJpeg/contents", image).fetch("softmax").run().get(0)) {
final long[] rshape = result.shape();
if (result.numDimensions() != 2 || rshape[0] != 1) {
throw new RuntimeException(
String.format(
"Expected model to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape %s",
Arrays.toString(rshape)));
}
int nlabels = (int) rshape[1];
return result.copyTo(new float[1][nlabels])[0];
}
}
}代码示例来源：origin: org.bytedeco.javacpp-presets/tensorflow
private static float[] executeInceptionGraph(byte[] graphDef, Tensor<Float> image) {
try (Graph g = new Graph()) {
g.importGraphDef(graphDef);
try (Session s = new Session(g);
// Generally, there may be multiple output tensors, all of them must be closed to prevent resource leaks.
Tensor<Float> result =
s.runner().feed("input", image).fetch("output").run().get(0).expect(Float.class)) {
final long[] rshape = result.shape();
if (result.numDimensions() != 2 || rshape[0] != 1) {
throw new RuntimeException(
String.format(
"Expected model to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape %s",
Arrays.toString(rshape)));
}
int nlabels = (int) rshape[1];
return result.copyTo(new float[1][nlabels])[0];
}
}
}代码示例来源：origin: biezhi/java-library-examples
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"));
}
}
}
}代码示例来源：origin: biezhi/java-library-examples
public static void main(String[] args) {
/**
* 定义一个 graph 类，并在这张图上定义了 foo 与 bar 的两个变量
*/
try (Graph g = new Graph()) {
try (Tensor<Integer> t = Tensor.create(30, Integer.class)) {
g.opBuilder("Const", "foo").setAttr("dtype", t.dataType()).setAttr("value", t).build();
}
try (Tensor<Integer> t = Tensor.create(20, Integer.class)) {
g.opBuilder("Const", "bar").setAttr("dtype", t.dataType()).setAttr("value", t).build();
}
try (Session s = new Session(g);
Tensor output1 = s.runner().fetch("foo").run().get(0);
Tensor output2 = s.runner().fetch("bar").run().get(0)) {
System.out.println(output1.intValue());
System.out.println(output2.intValue());
}
}
}
}代码示例来源：origin: org.springframework.cloud.stream.app/spring-cloud-starter-stream-processor-image-recognition
public ImageRecognitionTensorflowInputConverter() {
graph = new Graph();
GraphBuilder b = new GraphBuilder(graph);
// Some constants specific to the pre-trained model at:
// https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
// - The model was trained with images scaled to 224x224 pixels.
// - The colors, represented as R, G, B in 1-byte each were converted to
// float using (value - Mean)/Scale.
final int H = 224;
final int W = 224;
final float mean = 117f;
final float scale = 1f;
final Output input = b.placeholder("input", DataType.STRING);
graphOutput =
b.div(
b.sub(
b.resizeBilinear(
b.expandDims(
b.cast(b.decodeJpeg(input, 3), DataType.FLOAT),
b.constant("make_batch", 0)),
b.constant("size", new int[] { H, W })),
b.constant("mean", mean)),
b.constant("scale", scale));
}代码示例来源：origin: spring-cloud-stream-app-starters/tensorflow
public ImageRecognitionTensorflowInputConverter() {
graph = new Graph();
GraphBuilder b = new GraphBuilder(graph);
// Some constants specific to the pre-trained model at:
// https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
// - The model was trained with images scaled to 224x224 pixels.
// - The colors, represented as R, G, B in 1-byte each were converted to
// float using (value - Mean)/Scale.
final int H = 224;
final int W = 224;
final float mean = 117f;
final float scale = 1f;
final Output input = b.placeholder("input", DataType.STRING);
graphOutput =
b.div(
b.sub(
b.resizeBilinear(
b.expandDims(
b.cast(b.decodeJpeg(input, 3), DataType.FLOAT),
b.constant("make_batch", 0)),
b.constant("size", new int[] { H, W })),
b.constant("mean", mean)),
b.constant("scale", scale));
}代码示例来源：origin: jdye64/nifi-addons
private Tensor constructAndExecuteGraphToNormalizeImage(byte[] imageBytes) {
try (Graph g = new Graph()) {
GraphBuilder b = new GraphBuilder(g);代码示例来源：origin: org.bytedeco.javacpp-presets/tensorflow
private static Tensor<Float> constructAndExecuteGraphToNormalizeImage(byte[] imageBytes) {
try (Graph g = new Graph()) {
GraphBuilder b = new GraphBuilder(g);内容来源于网络，如有侵权，请联系作者删除！
