TensorFlow 迁移学习 ValueError

Question

我在谷歌 colab 上尝试迁移学习，解决图像分类问题，然后运行以下代码：

# Setup input shape to the model
INPUT_SHAPE = [None, 244, 244, 3] # batch, height, width, colour channels

# Setup output shape of the model
OUTPUT_SHAPE = 120

# Setup model URL form TensorFlow Hub
MODEL_URL =  "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"

# Create a function which builds a Keras model
def create_model(input_shape=INPUT_SHAPE, output_shape=OUTPUT_SHAPE, model_url=MODEL_URL):
  print("Building model with:", MODEL_URL)

  # Setup the model layers
  model = tf.keras.Sequential([
    hub.KerasLayer(MODEL_URL), # Layer 1 (input layer)
    tf.keras.layers.Dense(units=OUTPUT_SHAPE,
                          activation="softmax") # Layer 2 (output layer)
  ])

  # Compile the model
  model.compile(
      loss=tf.keras.losses.CategoricalCrossentropy(),
      optimizer=tf.keras.optimizers.Adam(),
      metrics=["accuracy"]
  )

  # Build the model
  model.build(INPUT_SHAPE)

  return model

model = create_model()
model.summary()

我收到了这个错误：

Building model with: https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-43-0fd4f47c95c0> in <module>()
----> 1 model = create_model()
      2 model.summary()

5 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    263       except Exception as e:  # pylint:disable=broad-except
    264         if hasattr(e, 'ag_error_metadata'):
--> 265           raise e.ag_error_metadata.to_exception(e)
    266         else:
    267           raise

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow_hub/keras_layer.py:229 call  *
        result = smart_cond.smart_cond(training,
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:486 _call_attribute  **
        return instance.__call__(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:580 __call__
        result = self._call(*args, **kwds)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:627 _call
        self._initialize(args, kwds, add_initializers_to=initializers)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:506 _initialize
        *args, **kwds))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:2446 _get_concrete_function_internal_garbage_collected
        graph_function, _, _ = self._maybe_define_function(args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:2777 _maybe_define_function
        graph_function = self._create_graph_function(args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:2667 _create_graph_function
        capture_by_value=self._capture_by_value),
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:981 func_graph_from_py_func
        func_outputs = python_func(*func_args, **func_kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:441 wrapped_fn
        return weak_wrapped_fn().__wrapped__(*args, **kwds)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/function_deserialization.py:261 restored_function_body
        "\n\n".join(signature_descriptions)))

    ValueError: Could not find matching function to call loaded from the SavedModel. Got:
      Positional arguments (4 total):
        * Tensor("inputs:0", shape=(None, 244, 244, 3), dtype=float32)
        * False
        * False
        * 0.99
      Keyword arguments: {}
    
    Expected these arguments to match one of the following 4 option(s):
    
    Option 1:
      Positional arguments (4 total):
        * TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        * False
        * True
        * TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
      Keyword arguments: {}
    
    Option 2:
      Positional arguments (4 total):
        * TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        * True
        * False
        * TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
      Keyword arguments: {}
    
    Option 3:
      Positional arguments (4 total):
        * TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        * True
        * True
        * TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
      Keyword arguments: {}
    
    Option 4:
      Positional arguments (4 total):
        * TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
        * False
        * False
        * TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
      Keyword arguments: {}

我尝试安装 tf-nightly 和旧版本的 tensorflow 以查看它是否会运行，但这不起作用。我还尝试了旧版本的 tensorflow_hub，这也导致了更多错误。我试图将笔记本恢复出厂设置并重试，但我得到了同样的错误。如果我注释掉 model.build(INPUT_SHAPE) ，错误不会出现。除此之外，我不确定如何解决此问题。

Answer 1

您的代码需要进行两项更改才能正常工作。

1. 我们应该使用MobileNet_V2_Feature_Vector 模块而不是MobileNet_V2_Image_Classification 模块，因为您希望将MobileNet 用于您的数据集，而不是在ImageNet Dataset 上使用它。
2. 在使用MobileNet_V2_Feature_Vector模块时，我们应该使用Input Layer，如下所示：

    tf.keras.layers.InputLayer(input_shape=(224,224,3))

完整的工作代码如下所示：

import tensorflow as tf
import tensorflow_hub as hub

# Setup input shape to the model
INPUT_SHAPE = [None, 244, 244, 3] # batch, height, width, colour channels

# Setup output shape of the model
OUTPUT_SHAPE = 120

# Setup model URL form TensorFlow Hub
MODEL_URL =  "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"

# Create a function which builds a Keras model
def create_model(input_shape=INPUT_SHAPE, output_shape=OUTPUT_SHAPE, model_url=MODEL_URL):
  print("Building model with:", MODEL_URL)

  # Setup the model layers
  model = tf.keras.Sequential([tf.keras.layers.InputLayer(input_shape=(224,224,3)),
    hub.KerasLayer(MODEL_URL, output_shape=[1280],
                   trainable=False), 
    tf.keras.layers.Dense(units=OUTPUT_SHAPE,
                          activation="softmax")
  ])

  # Compile the model
  model.compile(
      loss=tf.keras.losses.CategoricalCrossentropy(),
      optimizer=tf.keras.optimizers.Adam(),
      metrics=["accuracy"]
  )

  # Build the model
  model.build(INPUT_SHAPE)

  return model

model = create_model()
model.summary()

以上代码的输出如下所示：

Building model with: https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4
Model: "sequential_8"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
keras_layer_8 (KerasLayer)   (None, 1280)              2257984   
_________________________________________________________________
dense_8 (Dense)              (None, 120)               153720    
=================================================================
Total params: 2,411,704
Trainable params: 153,720
Non-trainable params: 2,257,984

请参阅此综合TF Hub Tutorial，了解有关将MobileNet_V2 用于您的Dataset 的更多详细信息。