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我在 tf-hub 上使用 Tensorflow 2 和 LaBSE 预训练模型（两者都不太熟悉）。（https://tfhub.dev/google/LaBSE/2）。我正在尝试使用自定义文本数据集训练多类分类器。我也在 BERT 分类器（https://www.tensorflow.org/text/tutorials/classify_text_with_bert）上关注这个例子，以了解模型是如何构建的。这是为了检查只有我可以训练和运行模型。我正在为从 csv 数据获得的输入文本使用数据集对象，如下所示，

"Sentence","label"
"sentence sample1", 0
"sentence sample2", 3

我像往常一样把它们分成 X, y 组。但是，在尝试训练模型时出现上述错误。下面是我的代码，

def build_classifier_model():
  text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
  preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='preprocessing')
  encoder_inputs = preprocessing_layer(text_input)
  encoder = hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='LaBSE_encoder')
  outputs = encoder(encoder_inputs)
  net = outputs['pooled_output']
  activation= tf.keras.activations.softmax#None
  net = tf.keras.layers.Dropout(0.1)(net)
  net = tf.keras.layers.Dense(4, activation=activation, name='classifier')(net)
  return tf.keras.Model(text_input, net)

loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
metrics = tf.keras.metrics.SparseCategoricalAccuracy()
epochs = 5
optimizer=tf.keras.optimizers.Adam()

train_dataset = tf.data.Dataset.from_tensor_slices(( # convert to dataset objects
    np.array(X_train),np.array(y_train,dtype='int32')
  
))

test_dataset = tf.data.Dataset.from_tensor_slices((
    np.array(X_test),np.array(y_test,dtype='int32')
    
))

这些数据集对象的规格是，<TensorSliceDataset 形状：（（），（）），类型：（tf.string，tf.int32）>


classifier_model.compile(optimizer=optimizer,
                         loss=loss,
                         metrics=metrics)

his = classifier_model.fit(train_dataset, validation_data=test_dataset,
                               epochs=epochs, batch_size=8) #ignore that I'm using test dataset for validation dataset

最后一步给出了错误；

Epoch 1/5
WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='text'), name='text', description="created by layer 'text'"), but it was called on an input with incompatible shape ().
WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='text'), name='text', description="created by layer 'text'"), but it was called on an input with incompatible shape ().
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-141-3523a14b56f1> in <module>()
      1 history = classifier_model.fit(train_dataset, validation_data=test_dataset,
----> 2                                epochs=epochs, batch_size=8)

9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    984           except Exception as e:  # pylint:disable=broad-except
    985             if hasattr(e, "ag_error_metadata"):
--> 986               raise e.ag_error_metadata.to_exception(e)
    987             else:
    988               raise

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:855 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/tensorflow_hub/keras_layer.py:237 call  *
        result = smart_cond.smart_cond(training,
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/load.py:670 _call_attribute  **
        return instance.__call__(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:889 __call__
        result = self._call(*args, **kwds)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:924 _call
        results = self._stateful_fn(*args, **kwds)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py:3022 __call__
        filtered_flat_args) = self._maybe_define_function(args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py:3444 _maybe_define_function
        graph_function = self._create_graph_function(args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py:3289 _create_graph_function
        capture_by_value=self._capture_by_value),
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py:999 func_graph_from_py_func
        func_outputs = python_func(*func_args, **func_kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:672 wrapped_fn
        out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/function_deserialization.py:291 restored_function_body
        "\n\n".join(signature_descriptions)))

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

我认为这是提供给输入的数据集对象规范的问题，但不了解如何修复它或确切原因。即使我的数据集对象具有“tf.string”类型，我也不明白为什么它与预期的输入不兼容。我查看了现有的答案，由于我对 TF 不太熟悉，我想知道原因是什么以及如何解决这个问题。

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