TensorFlow 2.0，keras.applications中出错（未知TensorShape上未定义as_list（））

关于此错误，有几个问题：

ValueError: as_list() is not defined on an unknown TensorShape.

还有一些关于git的相关问题：

然而，我还没有找到一个一致的答案来解释为什么会出现这条消息，也没有找到解决我的具体问题的方法。整个管道过去使用的是tf2.0.0-alpha，现在，在使用Conda

Conda install tensorflow=2.0 python=3.6安装后，管道中断

简而言之，我使用生成器将图像数据返回到
tf.data.Dataset.from_generator（）
方法。这很好，直到我尝试调用
model.fit（）
方法，导致以下错误

Train for 750 steps, validate for 100 steps
Epoch 1/5
  1/750 [..............................] - ETA: 10sTraceback (most recent call last):
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/Users/tmo/Projects/casa/image/src/train.py", line 148, in <module>
    Trainer().train_vgg16()
  File "/Users/tmo/Projects/casa/image/src/train.py", line 142, in train_vgg16
    validation_steps=100)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py", line 728, in fit
    use_multiprocessing=use_multiprocessing)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 324, in fit
    total_epochs=epochs)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 123, in run_one_epoch
    batch_outs = execution_function(iterator)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 86, in execution_function
    distributed_function(input_fn))
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 457, in __call__
    result = self._call(*args, **kwds)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 503, in _call
    self._initialize(args, kwds, add_initializers_to=initializer_map)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 408, in _initialize
    *args, **kwds))
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 1848, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2150, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2041, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 915, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 358, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 66, in distributed_function
    model, input_iterator, mode)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 112, in _prepare_feed_values
    inputs, targets, sample_weights = _get_input_from_iterator(inputs)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 149, in _get_input_from_iterator
    distribution_strategy_context.get_strategy(), x, y, sample_weights)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py", line 308, in validate_distributed_dataset_inputs
    x_values_list = validate_per_replica_inputs(distribution_strategy, x)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py", line 356, in validate_per_replica_inputs
    validate_all_tensor_shapes(x, x_values)
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py", line 373, in validate_all_tensor_shapes
    x_shape = x_values[0].shape.as_list()
  File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_shape.py", line 1171, in as_list
    raise ValueError("as_list() is not defined on an unknown TensorShape.")
ValueError: as_list() is not defined on an unknown TensorShape.

应用于生成器（例如，
training_generator
），该生成器运行图像列表并生成预处理结果：

    def make_ts_dataset(self):

        AUTOTUNE = tf.data.experimental.AUTOTUNE

        BATCH_SIZE = 32
        image_count_training = len(self.X_train)
        image_count_validation = len(self.X_test)

        training_generator = GetTensor(hw=self.hw, train=True).make_tensor

        training_image_ds = tf.data.Dataset.from_generator(training_generator, tf.float32, [224, 224, 3])
        training_price_ds = tf.data.Dataset.from_tensor_slices(tf.cast(self.y_train, tf.float32))

        validation_generator = GetTensor(hw=self.hw, test=True).make_tensor

        validation_image_ds = tf.data.Dataset.from_generator(validation_generator, tf.float32, [224, 224, 3])
        validation_price_ds = tf.data.Dataset.from_tensor_slices(tf.cast(self.y_test, tf.float32))

        training_ds = tf.data.Dataset.zip((training_image_ds, training_price_ds))
        validation_ds = tf.data.Dataset.zip((validation_image_ds, validation_price_ds))

        training_ds = training_ds.shuffle(buffer_size=int(round(image_count_training)))
        training_ds = training_ds.repeat()
        training_ds = training_ds.batch(BATCH_SIZE)
        training_ds = training_ds.prefetch(buffer_size=AUTOTUNE)

        validation_ds = validation_ds.shuffle(buffer_size=int(round(image_count_validation)))
        validation_ds = validation_ds.repeat()
        validation_ds = validation_ds.batch(BATCH_SIZE)
        validation_ds = validation_ds.prefetch(buffer_size=AUTOTUNE)

        for image_batch, label_batch in training_ds.take(1):
            print(label_batch.shape, image_batch.shape)
            pass

        return training_ds, validation_ds

在所有点上，形状看起来都是正确的，即
（32，）（322242243）

我正在使用
VGG16

    def train_vgg16(self):

        training_ds, validation_ds = Trainer.make_ts_dataset(self)

        base_vgg = keras.applications.vgg16.VGG16(include_top=False, 
                                                  weights='imagenet', 
                                                  input_shape=(224, 224, 3))
        base_vgg.trainable = False

        print(base_vgg.summary())

        vgg_with_base = keras.Sequential([
            base_vgg,
            tf.keras.layers.GlobalMaxPooling2D(),
            tf.keras.layers.Dense(1024, activation=tf.nn.relu),
            tf.keras.layers.Dense(1024, activation=tf.nn.relu),
            tf.keras.layers.Dense(512, activation=tf.nn.relu),
            tf.keras.layers.Dense(1)])

        print(base_vgg.summary())

        vgg_with_base.compile(optimizer='adam',
                              loss='mse',
                              metrics=['mape'])

        vgg_with_base.fit(training_ds,
                          epochs=5,
                          validation_data=validation_ds,
                          steps_per_epoch=750,
                          validation_steps=100)

但是，培训从未启动，因为
x\u shape=x\u值[0]。shape.as\u list（）
失败

编辑（19年12月11日）：

经过一些故障排除，我发现错误是在
keras.applications
层中启动的

        base_vgg = keras.applications.vgg16.VGG16(include_top=False, 
                                                  weights='imagenet', 
                                                  input_shape=(224, 224, 3))

从模型中删除
base_vgg
并初始化培训效果良好
 使用
keras.applications.vgg16.vgg16
中的
tensorflow.keras.layers
中的
Input
明确定义形状，为我解决了这个问题

from tensorflow.keras.layers import Input

        base_vgg = keras.applications.vgg16.VGG16(include_top=False, 
                                                  weights='imagenet', 
                                                  input_tensor=Input(shape=(224,224,3)),
                                                  input_shape=(224, 224, 3))

我仍然认为这更接近于一个bug，而不是一个特性。
通过
keras.applications.vgg16.vgg16
中的
tensorflow.keras.layers
中的
Input
明确定义形状，为我解决了这个问题

from tensorflow.keras.layers import Input

        base_vgg = keras.applications.vgg16.VGG16(include_top=False, 
                                                  weights='imagenet', 
                                                  input_tensor=Input(shape=(224,224,3)),
                                                  input_shape=(224, 224, 3))

我仍然认为这更接近于一个bug而不是一个特性