1

我为自定义数据集重新训练了 inception v3 模型。但是在重新训练后，当我查看 TenosorGraph 时，我发现添加了一个名为 reshape 的层，然后是一个完全连接的层。我必须使用 snapdragonneural 处理引擎 (SNPE) 在嵌入式设备上运行模型，但它目前不支持重塑层以在 DSP 上运行。

是否有可能在不添加重塑层的情况下重新训练 inception v3。下面是添加了重塑层的再训练代码。

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,
      }

完整的代码可在此处获得： https ://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py

4

2 回答 2

1

从 SNPE SDK v1.8.0 开始，TensorFlow 的reshape层得到支持。

于 2017-12-06T08:53:23.897 回答
0

他们没有添加重塑层，而是从经过训练的模型中选择重塑层。之后，他们将在该重塑层的输出之上添加自己的层。

如果要选择更高层，请将 'pool_3/_reshape:0' 替换为所需层的名称。您应该能够从模型代码中推断出名称：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您想要的：

    for node in graph_def.node:
        print(node.name)
于 2017-09-20T08:54:18.163 回答