How to add an attention layer to LSTM autoencoder built as sequential keras model in python?

So I want to build an autoencoder model for sequence data. I have started to build a sequential keras model in python and now I want to add an attention layer in the middle, but have no idea how to approach this. My model so far:

from keras.layers import LSTM, TimeDistributed, RepeatVector, Layer
from keras.models import Sequential
import keras.backend as K

model = Sequential()
model.add(LSTM(20, activation="relu", input_shape=(time_steps,n_features), return_sequences=False))
model.add(RepeatVector(time_steps, name="bottleneck_output"))
model.add(LSTM(30, activation="relu", return_sequences=True))
model.add(TimeDistributed(Dense(n_features)))

model.compile(optimizer="adam", loss="mae")

So far I have tried to add an attention function copied from here

class attention(Layer):
    def __init__(self,**kwargs):
        super(attention,self).__init__(**kwargs)

    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):
        et=K.squeeze(K.tanh(K.dot(x,self.W)+self.b),axis=-1)
        at=K.softmax(et)
        at=K.expand_dims(at,axis=-1)
        output=x*at
        return K.sum(output,axis=1)

    def compute_output_shape(self,input_shape):
        return (input_shape[0],input_shape[-1])

    def get_config(self):
        return super(attention,self).get_config()

and added it after first LSTM, before repeat vector, i.e:

model = Sequential()
model.add(LSTM(20, activation="relu", input_shape=(time_steps,n_features), return_sequences=False))
model.add(attention()) # this is added
model.add(RepeatVector(time_steps, name="bottleneck_output"))
model.add(LSTM(30, activation="relu", return_sequences=True))
model.add(TimeDistributed(Dense(n_features)))

model.compile(optimizer="adam", loss="mae")

but the code gives error, because the dimensions somehow do not fit and the problem is in putting output of attention() to repeat vector:

ValueError: Input 0 is incompatible with layer bottleneck_output: expected ndim=2, found ndim=1

…. but according to model.summary() the output dimension of attention layer is (None, 20), which is the same also for the first lstm_1 layer . The code works without attention layer.

I would appreciate also some explanation why the solution is the solution to the problem, I am fairly new to python and have problems understanding what the class attention() is doing. I just copied it and tried to use it which is ofcrs not working….

Answer

Ok, I solved it. There has to be return_sequence = True in first LSTM layer. Then it works as it is.