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我是深度学习和编写模型的新手，最终我想使用 lstm 和注意力机制按时间提取输入权重。我的数据是具有可变长度序列（多对一）的时间序列数据和分类问题。

我的输入形状是(None , number of features) =(None,11) 我使用了参差不齐的张量和keras框架，并且在尝试使模型工作时遇到了尺寸问题。

我不确定在构建模型之前如何查看每一层的形状，我不确定问题出在哪里。

这是我尝试使用的代码

#custom attention
class attention(Layer):

    def __init__(self, return_sequences=True):
        self.return_sequences = return_sequences
        super(attention,self).__init__()
    
    def build(self, input_shape):
    
        self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
                               initializer="normal")
        self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
                               initializer="zeros")
    
        super(attention,self).build(input_shape)
    
    def call(self, x):
    
        e = K.tanh(K.dot(x,self.W)+self.b)
        a = K.softmax(e, axis=1)
        output = x*a
    
        if self.return_sequences:
            return output
    
        return K.sum(output, axis=1)
    

#input
sequence_input = tf.keras.layers.Input(shape=(None,11), dtype=tf.float32,ragged=True)
rnn_cell_size =64

#define layers
lstm = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM
                                     (rnn_cell_size,
                                      dropout=0.3,
                                      return_sequences=True,
                                      return_state=True), name="bi_lstm_0")(sequence_input)


lstm, forward_h, forward_c, backward_h, backward_c = tf.keras.layers.Bidirectional (tf.keras.layers.LSTM
     (rnn_cell_size,
      dropout=0.2,
      return_sequences=True,
      return_state=True,
      recurrent_activation='relu',
      recurrent_initializer='glorot_uniform'))(lstm)

state_h = tf.keras.layers.Concatenate()([forward_h, backward_h])
state_c = tf.keras.layers.Concatenate()([forward_c, backward_c])


context_vector, attention_weights = attention(32)(lstm, state_h)
output = keras.layers.Dense(2, activation='sigmoid')(context_vector)

model = keras.Model(inputs=sequence_input, outputs=output)

错误

The mask that was passed in was [None, None, None, None, None] and cannot be applied to RaggedTensor inputs. Please make sure that there is no mask passed in by upstream layers.
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