无整形层的再训练初始v3模型

Question

我已经为自定义数据集重新训练了inception v3模型。但是在重新训练之后，当我查看TenosorGraph时，我发现添加了一个名为reshape的层，后面跟着一个完全连接的层。我不得不在使用snapdragonneural神经处理引擎(SNPE)的嵌入式设备上运行该模型，但它目前还不支持在DSP上运行重形层。

有没有可能在不增加重塑层的情况下重新训练初始v3。下面是重新训练代码，其中添加了重塑图层。

enter code here
              def create_model_info(architecture):
  """Given the name of a model architecture, returns information about it.

  There are different base image recognition pretrained models that can be
  retrained using transfer learning, and this function translates from the name
  of a model to the attributes that are needed to download and train with it.

  Args:
    architecture: Name of a model architecture.

  Returns:
    Dictionary of information about the model, or None if the name isn't
    recognized

  Raises:
    ValueError: If architecture name is unknown.
  """
  architecture = architecture.lower()
  if architecture == 'inception_v3':
    # pylint: disable=line-too-long
    data_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
    # pylint: enable=line-too-long
    bottleneck_tensor_name = 'pool_3/_reshape:0'
    bottleneck_tensor_size = 2048
    input_width = 299
    input_height = 299
    input_depth = 3
    resized_input_tensor_name = 'Mul:0'
    model_file_name = 'classify_image_graph_def.pb'
    input_mean = 128
    input_std = 128
      elif architecture.startswith('mobilenet_'):
        parts = architecture.split('_')
        if len(parts) != 3 and len(parts) != 4:
          tf.logging.error("Couldn't understand architecture name '%s'",
                           architecture)
          return None
        version_string = parts[1]
        if (version_string != '1.0' and version_string != '0.75' and
            version_string != '0.50' and version_string != '0.25'):
          tf.logging.error(
              """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25',
      but found '%s' for architecture '%s'""",
              version_string, architecture)
          return None
        size_string = parts[2]
        if (size_string != '224' and size_string != '192' and
            size_string != '160' and size_string != '128'):
          tf.logging.error(
              """The Mobilenet input size should be '224', '192', '160', or '128',
     but found '%s' for architecture '%s'""",
              size_string, architecture)
          return None
        if len(parts) == 3:
          is_quantized = False
        else:
          if parts[3] != 'quantized':
            tf.logging.error(
                "Couldn't understand architecture suffix '%s' for '%s'", parts[3],
                architecture)
            return None
          is_quantized = True
        data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
        data_url += version_string + '_' + size_string + '_frozen.tgz'
        bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
        bottleneck_tensor_size = 1001
        input_width = int(size_string)
        input_height = int(size_string)
        input_depth = 3
        resized_input_tensor_name = 'input:0'
        if is_quantized:
          model_base_name = 'quantized_graph.pb'
        else:
          model_base_name = 'frozen_graph.pb'
        model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string
        model_file_name = os.path.join(model_dir_name, model_base_name)
        input_mean = 127.5
        input_std = 127.5
      else:
        tf.logging.error("Couldn't understand architecture name '%s'", architecture)
        raise ValueError('Unknown architecture', architecture)

      return {
          'data_url': data_url,
          'bottleneck_tensor_name': bottleneck_tensor_name,
          'bottleneck_tensor_size': bottleneck_tensor_size,
          'input_width': input_width,
          'input_height': input_height,
          'input_depth': input_depth,
          'resized_input_tensor_name': resized_input_tensor_name,
          'model_file_name': model_file_name,
          'input_mean': input_mean,
          'input_std': input_std,
      }

此处提供了compelete代码：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py

Answer 1

从SNPE SDK v1.8.0开始支持TensorFlow的reshape层。

Answer 2

他们不是在添加重塑图层，而是从训练好的模型中选择重塑图层。然后，他们将在重塑图层的输出上添加自己的图层。

如果您想选择一个更高的层，用您想要的层的名称替换'pool_3/_reshape:0‘。您应该能够从模型代码推导出名称：https://github.com/tensorflow/models/blob/master/slim/nets/inception_v3.py

或者更简单，打印graph_def中所有节点的名称并选择所需的节点：

    for node in graph_def.node:
        print(node.name)