使用python API进行的培训作为Java API中LabelImage模块的输入？

我有java tensorflow API的问题.我使用python tensorflow API运行训练,生成文件output_graph.pb和output_labels.txt.现在出于某种原因,我想使用这些文件作为java tensorflow API中LabelImage模块的输入.我认为一切都会正常工作,因为该模块只需要一个.pb和一个.txt.不过,当我运行模块时,我收到此错误：

2017-04-26 10:12:56.711402: W tensorflow/core/framework/op_def_util.cc:332] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
Exception in thread "main" java.lang.IllegalArgumentException: No Operation named [input] in the Graph
at org.tensorflow.Session$Runner.operationByName(Session.java:343)
at org.tensorflow.Session$Runner.feed(Session.java:137)
at org.tensorflow.Session$Runner.feed(Session.java:126)
at it.zero11.LabelImage.executeInceptionGraph(LabelImage.java:115)
at it.zero11.LabelImage.main(LabelImage.java:68)

如果你帮助我找到问题所在,我将非常感激.此外,我想问你是否有办法从java tensorflow API运行培训,因为这会使事情变得更容易.

更确切地说：

事实上,我不使用自编代码,至少对于相关步骤.我所做的就是使用这个模块https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py进行培训,将其包含在包含根据其描述在子目录之间划分的图像的目录中.特别是,我认为这些是产生输出的线：

output_graph_def = graph_util.convert_variables_to_constants(
    sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
with gfile.FastGFile(FLAGS.output_graph, 'wb') as f:
  f.write(output_graph_def.SerializeToString())
with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
  f.write('\n'.join(image_lists.keys()) + '\n')

然后,我将输出(一个some_graph.pb和一个some_labels.txt)作为此java模块的输入：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/examples/LabelImage.java,替换默认输入.我得到的错误是上面报告的错误.

解决方法:

LabelImage.java中默认使用的模型与正在重新训练的模型不同,因此输入和输出节点的名称不对齐.请注意,TensorFlow模型是图形,feed()和fetch()的参数是图形中节点的名称.因此,您需要知道适合您的模型的名称.

看一下retrain.py,它似乎有一个节点,它将JPEG文件的原始内容作为输入(节点DecodeJpeg/contents),并在节点final_result中生成标签集.

如果是这种情况,那么你会在Java中执行类似下面的操作(并且您不需要构造图形来对图像进行标准化,因为这似乎是重新训练模型的一部分,因此将LabelImage.java:64替换为某些内容喜欢：

try (Tensor image = Tensor.create(imageBytes);
     Graph g = new Graph()) {
  g.importGraphDef(graphDef);
  try (Session s = new Session(g);
    // Note the change to the name of the node and the fact
    // that it is being provided the raw imageBytes as input
    Tensor result = s.runner().feed("DecodeJpeg/contents", image).fetch("final_result").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];
    float[] probabilities = result.copyTo(new float[1][nlabels])[0];
    // At this point nlabels = number of classes in your retrained model
    DoSomethingWith(probabilities);
  }
}

希望有所帮助.

标签：image-recognition,java,python,tensorflow
来源： https://codeday.me/bug/20191007/1864494.html