我现在正试图在Keras之上构建seq2seq模型。
我提到了这个official seq2seq example，但我想知道为什么不使用TimeDistibuted图层而不是Dense。
# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None, num_decoder_tokens))
# 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 = Dense(num_decoder_tokens, activation='softmax') # <- Here!
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)
Seq2seq模型必须处理多对多问题，因此每个时间步都必须单独处理。因此，我认为此模型应该有TimeDistributed(Dense())图层，但实际上，它是Dense。
有人可以向我解释原因吗？