Python InvalidArgumentError:维度0的切片索引0超出顺序模型中自定义层的边界

获取此错误

InvalidArgumentError: slice index 0 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/

调用方法的输出与第一个密集层的输入相比存在一些问题。将输出从“tf.constant[results]”更改为“tf.constant[results]”只会给出错误“min_ndim=2”，得到ndim=1

class TextVectorizationLayer(keras.layers.Layer):

   def __init__(self, **kwargs):
      super().__init__(**kwargs, dynamic=True)
       self.table = {}

   def call(self, inputs, **kwargs):
       review = preprocess(inputs)
       results = []

       for word in self.table:
           if word in review:
               results.append(self.table.get(word))
           else:
               results.append(0)

       return tf.constant([results])

   def adapt(self, data, count):
           reviews = [preprocess(r) for (r,_) in data]
           for review in reviews:
               for word in review.numpy():
                   self.table[word] = \
                       self.table.get(word, 0) + 1

           self.table = OrderedDict(sorted(self.table.items(),
                                 key=lambda x: x[1],
                                 reverse=True)[:count])

           return self.table

sample_string_batches = train_set.take(25)
vectorization = TextVectorizationLayer()
words = vectorization.adapt(sample_string_batches, 400)

model = keras.models.Sequential([
    vectorization,
    keras.layers.Dense(100, activation="relu"),
    keras.layers.Dense(1, activation="sigmoid"),
])
model.compile(loss="binary_crossentropy", optimizer="nadam",
              metrics=["accuracy"])
model.fit(train_set, epochs=5, validation_data=val_set)

列车和Val数据的形状为（（），（））

不可训练的参数：0

请检查层的“输入形状”参数，因为它将提供（0，x）形状。因此，获取索引0的错误

请检查层的“输入形状”参数，因为它将提供（0，x）形状。从而得到索引0的错误

Model: "sequential_15"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
text_vectorization_layer_10  multiple                  0         
_________________________________________________________________
dense_30 (Dense)             multiple                  40100     
_________________________________________________________________
dense_31 (Dense)             multiple                  101       
=================================================================
Total params: 40,201
Trainable params: 40,201