数据挖掘 - IndexError：列表索引超出范围 - 吾爱随笔录

我正在使用 RNN-VAE 架构实现一个序列 2 序列模型，并且我使用了注意力机制。我在解码器部分有问题。我正在努力解决这个错误：IndexError: list index out of range 当我运行这段代码时：

decoder_inputs = Input(shape=(len_target,))
decoder_emb = Embedding(input_dim=vocab_out_size, output_dim=embedding_dim)
decoder_lstm = LSTM(units=units, return_sequences=True, return_state=True)
decoder_lstm_out, _, _ = decoder_lstm(decoder_emb(decoder_inputs), 
             initial_state=encoder_states)

print("enc_outputs", encoder_outputs.shape) # ==> (?,256)
print("decoder_lstm_out", decoder_lstm_out.shape)# ==> (?,12,256)
print("zzzzzz", z.shape) # ==> (?,256)

attn_layer = AttentionLayer(name='attention_layer')
attn_out, attn_states = attn_layer([z,z], decoder_lstm_out)

错误在最后一行提出，并给出了回溯：

Traceback (most recent call last):
     File "malek_tuto.py", line 197, in <module>
           attn_out, attn_states = attn_layer([z,z], decoder_lstm_out)
     File "C:\Users\lightland\Anaconda3\lib\site- 
       packages\tensorflow\python\keras\engine\base_layer.py", line 728, in 
       __call__    self.build(input_shapes)
     File "D:\PFE\Contribution\modele\layers\attention.py", line 24, in 
        build shape=tf.TensorShape((input_shape[0][3], input_shape[0][3])),
     File "C:\Users\lightland\Anaconda3\lib\site- 
       packages\tensorflow\python\framework\tensor_shape.py", line 615, in 
         __getitem__   return self._dims[key]
     IndexError: list index out of range

在 AttentionLayer 类中，构建函数 id 定义为：

 def build(self, input_shape):

      assert isinstance(input_shape, list)
      print("hhhhhhhhhh",input_shape)
      print("jjknkjnjk")
      # Create a trainable weight variable for this layer.

       self.W_a = self.add_weight(name='W_a',
                               shape=tf.TensorShape((input_shape[0][2], 
                                input_shape[0][2])),
                               initializer='uniform',
                               trainable=True)
       self.U_a = self.add_weight(name='U_a',
                               shape=tf.TensorShape((input_shape[1][2], 
                               input_shape[0][2])),
                               initializer='uniform',
                               trainable=True)
       self.V_a = self.add_weight(name='V_a',
                               shape=tf.TensorShape((input_shape[0][2], 1)),
                               initializer='uniform',
                               trainable=True)

       super(AttentionLayer, self).build(input_shape)

如果有人可以帮助我，我将非常感激，我无法理解问题出在哪里，以及如何解决它。

提前致谢