What's the sequence length of a Keras Bidirectional layer?

Question

If I have:

        self.model.add(LSTM(lstm1_size, input_shape=(seq_length, feature_dim), return_sequences=True))
        self.model.add(BatchNormalization())
        self.model.add(Dropout(0.2))

then my seq_length specifies how many slices of data I want to process at once. If it matters, my model is a sequence-to-sequence (same size).

But if I have:

        self.model.add(Bidirectional(LSTM(lstm1_size, input_shape=(seq_length, feature_dim), return_sequences=True)))
        self.model.add(BatchNormalization())
        self.model.add(Dropout(0.2))

then is that doubling the sequence size? Or at each time step, is it getting seq_length / 2 before and after that timestep?

Answer 1

Using a bidirectional LSTM layer has no effect on the sequence length. I tested this with the following code:

from keras.models import Sequential
from keras.layers import Bidirectional,LSTM,BatchNormalization,Dropout,Input

model = Sequential()
lstm1_size = 50
seq_length = 128
feature_dim = 20
model.add(Bidirectional(LSTM(lstm1_size, input_shape=(seq_length, feature_dim), return_sequences=True)))
model.add(BatchNormalization())
model.add(Dropout(0.2))

batch_size = 32

model.build(input_shape=(batch_size,seq_length, feature_dim))

model.summary()

This resulted in the following output for bidirectional

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bidirectional_1 (Bidirection (32, 128, 100)            28400     
_________________________________________________________________
batch_normalization_1 (Batch (32, 128, 100)            400       
_________________________________________________________________
dropout_1 (Dropout)          (32, 128, 100)            0         
=================================================================
Total params: 28,800
Trainable params: 28,600
Non-trainable params: 200

No bidirectional layer:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_1 (LSTM)                (None, 128, 50)           14200     
_________________________________________________________________
batch_normalization_1 (Batch (None, 128, 50)           200       
_________________________________________________________________
dropout_1 (Dropout)          (None, 128, 50)           0         
=================================================================
Total params: 14,400
Trainable params: 14,300
Non-trainable params: 100
_________________________________________________________________