如何在LSTM中找到时间步长？

如何解决如何在LSTM中找到时间步长？

我有一个Bi-LSTM模型，我想弄清楚它的计算复杂性。我在互联网上读过

使用随机梯度下降（SGD）优化技术按权重和时间步学习LSTM模型的计算复杂度为O（1）。因此，每个时间步的学习计算复杂度为O（W）。

但是如何找到模型中的时间步长？我的模特是

model = Sequential()
model.add(Embedding(max_words,768,input_length=max_len,weights=[embedding]))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Bidirectional(LSTM(16)))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.6))
model.add(Dense(2,activation='softmax',use_bias=True,kernel_regularizer=regularizers.l1_l2(l1=1e-5,l2=1e-4),bias_regularizer=regularizers.l2(1e-4),activity_regularizer=regularizers.l2(1e-5)))
model.summary()

模型摘要是

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None,768)          37147392  
_________________________________________________________________
batch_normalization_2 (Batch (None,768)          3072      
_________________________________________________________________
activation_2 (Activation)    (None,768)          0         
_________________________________________________________________
bidirectional_1 (Bidirection (None,32)                100480    
_________________________________________________________________
batch_normalization_3 (Batch (None,32)                128       
_________________________________________________________________
activation_3 (Activation)    (None,32)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None,32)                0         
_________________________________________________________________
dense_1 (Dense)              (None,2)                 66        
=================================================================
Total params: 37,251,138
Trainable params: 37,249,538
Non-trainable params: 1,600