有一个双向LSTM模型，我不明白为什么在第二次实现model2.add（Bidirectional（LSTM（10，recurrent_dropout=0.2）））之后，结果我们得到了二维（None，20），但在第一个双向LSTM中我们得到了（None，409，20）。有谁能帮帮我吗？我如何在模型中添加自我关注层？

from tensorflow.keras.layers import LSTM,Dense, Dropout,Bidirectional
from tensorflow.keras.layers import SpatialDropout1D
from tensorflow.keras.layers import Embedding
from tensorflow.keras.preprocessing.text import Tokenizer

embedding_vector_length = 100

model2 = Sequential()

model2.add(Embedding(len(tokenizer.word_index) + 1, embedding_vector_length,     
                                         input_length=409) )

model2.add(Bidirectional(LSTM(10, return_sequences=True, recurrent_dropout=0.2)))
model2.add(Dropout(0.4))
model2.add(Bidirectional(LSTM(10, recurrent_dropout=0.2)))
model2.add(SeqSelfAttention())

#model.add(Dropout(dropout))
#model2.add(Dense(256, activation='relu'))

#model.add(Dropout(0.2))

model2.add(Dense(3, activation='softmax'))
model2.compile(loss='binary_crossentropy',optimizer='adam', 
                           metrics=['accuracy'])
print(model2.summary())

并且输出：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_23 (Embedding)     (None, 409, 100)          1766600   
_________________________________________________________________
bidirectional_12 (Bidirectio (None, 409, 20)           8880      
_________________________________________________________________
dropout_8 (Dropout)          (None, 409, 20)           0         
_________________________________________________________________
bidirectional_13 (Bidirectio (None, 20)                2480      
_________________________________________________________________
dense_15 (Dense)             (None, 3)                 63        
=================================================================
Total params: 1,778,023
Trainable params: 1,778,023
Non-trainable params: 0
_________________________________________________________________
None