TimeDistributed (Dense) против Dense в seq2seq

Question

Учитывая приведенный ниже код

encoder_inputs = Input(shape=(16, 70))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(59, 93))
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs,_,_ = decoder_lstm(decoder_inputs,
                                     initial_state=encoder_states)
decoder_dense = TimeDistributed(Dense(93, activation='softmax'))
decoder_outputs = decoder_dense(decoder_outputs)

# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

, если я изменю

decoder_dense = TimeDistributed(Dense(93, activation='softmax'))

на

decoder_dense = Dense(93, activation='softmax')

это все еще работает, но какой метод более эффективен?