TensorFlow:服务模型返回始终相同的预测

Question

我需要你的帮助，我现在有点卡住。

i重新训练一个分类张量流模型，可提供相当不错的结果。现在，我想通过TensorFlow服务提供它。我设法使用它，但是当我使用它时，无论输入是什么，它总是给我相同的结果。

我认为导出模型的方式有问题，但我不知道什么。以下是我的代码。

有人可以帮我吗？非常感谢大家

这是将我的输入映像转换为TF的可读对象的功能：

def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
            input_mean=0, input_std=255):
  input_name = "file_reader"
  output_name = "normalized"
  file_reader = tf.read_file(file_name, input_name)
  if file_name.endswith(".png"):
    image_reader = tf.image.decode_png(file_reader, channels = 3,
                                   name='png_reader')
  elif file_name.endswith(".gif"):
image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
                                              name='gif_reader'))
  elif file_name.endswith(".bmp"):
image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
  else:
image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
                                    name='jpeg_reader')
  float_caster = tf.cast(image_reader, tf.float32)
  dims_expander = tf.expand_dims(float_caster, 0);
  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
  sess = tf.Session()
  result = sess.run(normalized)
  return result,normalized

这就是我导出模型的方式：

  # Getting graph from the saved pb file
  graph = tf.Graph()
  graph_def = tf.GraphDef()
  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)
  # below, var "t" is the result of the transformation, "tf_input" a tensor before computation.
  t,predict_inputs_tensor = read_tensor_from_image_file(file_name,
                              input_height=input_height,
                              input_width=input_width,
                              input_mean=input_mean,
                              input_std=input_std)
  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name);
  output_operation = graph.get_operation_by_name(output_name);
  # Let's predict result to get an exemple output
  with tf.Session(graph=graph) as sess:
    results = sess.run(output_operation.outputs[0],
                  {input_operation.outputs[0]: t})
  results = np.squeeze(results)

  # Creating labels 
  class_descriptions = []
  labels = load_labels(label_file)
  for s in labels:
    class_descriptions.append(s)
  classes_output_tensor = tf.constant(class_descriptions)      
  table = 
 tf.contrib.lookup.index_to_string_table_from_tensor(classes_output_tensor)
 classes = table.lookup(tf.to_int64(labels))
  top_k = results.argsort()[-len(labels):][::-1]
  scores_output_tensor, indices =tf.nn.top_k(results, len(labels))
  # Display
  for i in top_k:
    print(labels[i], results[i])

  version=1
  path="/Users/dboudeau/depot/tensorflow-for-poets-2/tf_files"
  tf.app.flags.DEFINE_integer('version', version, 'version number of the model.')
  tf.app.flags.DEFINE_string('work_dir', path, 'your older model  directory.')
  tf.app.flags.DEFINE_string('model_dir', '/tmp/magic_model', 'saved model directory')
  FLAGS = tf.app.flags.FLAGS
  with tf.Session() as sess:
      classify_inputs_tensor_info = 
tf.saved_model.utils.build_tensor_info(predict_inputs_tensor)
  export_path = os.path.join(
      tf.compat.as_bytes(FLAGS.model_dir)
      ,tf.compat.as_bytes(str(FLAGS.version))
      )
  print(export_path)
  builder = tf.saved_model.builder.SavedModelBuilder(export_path)
  # define the signature def map here        

predition_inputs_tensor_info = tf.saved_model.utils.build_tensor_info（prectiv_inputs_tensor）class_output_tensor_info = tf.saved_model.utils.build_tensor_info（class_output_tensor）scores_output_tensor_info = tf.saved_model.utils.build_tensor_info（scores_output_tensor）

  classification_signature = (
    tf.saved_model.signature_def_utils.build_signature_def(
              inputs={
                  tf.saved_model.signature_constants.CLASSIFY_INPUTS:
                      classify_inputs_tensor_info
              },
              outputs={
                  tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
                      classes_output_tensor_info,
                  tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
                      scores_output_tensor_info
              },
              method_name=tf.saved_model.signature_constants.
              CLASSIFY_METHOD_NAME))
  prediction_signature = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs={'images': predict_inputs_tensor_info},
              outputs={
                  'classes': classes_output_tensor_info,
                  'scores': scores_output_tensor_info
              },
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
          ))
  legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
  # This one does'
  final_sdn={

tf.saved_model.signature_constants.default_serving_signature_def_key：classification_signature， }

  builder.add_meta_graph_and_variables(
      sess, [tf.saved_model.tag_constants.SERVING],
      signature_def_map=final_sdn,
      legacy_init_op=legacy_init_op)
  builder.save()

Answer 1

我遇到了一段时间以来与之抗衡的问题。最后，事实证明，我正在模型中的float32发送双重类型，并且以某种方式将TensowFlow铸造为Double到0值。这意味着，无论您通过RPC发送什么，在模型中都将其视为0。希望它有帮助。

Answer 2

如果任何人仍在寻找其他答案，请查看以下答案：

张量子流在预处理的keras resnet50模型上返回相同的预测

我也面临同一问题，我尝试了所有其他选项，例如检查预处理功能，将输入DTYPE从uint更改为float32/64。我以为是由于辍学而发生的，但是事实证明它正在发生，因为Tensorflow的global_variables_initializer。