How to add attention mechanism to my sequence-to-sequence architecture in Keras?
By default an LSTM model returns only the output of last timestep.
model = Sequential()
model.add(LSTM(hidden_nodes, input_shape=(n_timesteps, n_features)))
##output shape is (n_features)

So the below step is needed to repeat the output vector 'n' number of times where 'n' should be the number of time-steps
model.add(RepeatVector(n_timesteps))
##now shape becomes (n_timesteps,n_features)

But when you specify 'return_sequences=True', LSTM returns a hidden state for ALL timesteps. The output shape from the LSTM directly is (n_timesteps,n_features). So you DONT need to to a 'Repeat Vector'
So to eliminate the error, just remove line 4
Edit - I would recommend using the 'return_sequences=true' option and NOT using Repeatvector option even though the latter may compile. This will lead to better results as you are passing far more data across timesteps to the next layer and this is the accepted approach for most situations