I'm trying to create an autoencoder in keras with bucketing where the input and the output have different time steps.
model = Sequential()
#encoder
model.add(Embedding(vocab_size, embedding_size, mask_zero=True))
model.add(LSTM(units=hidden_size, return_sequences=False))
#decoder
model.add(RepeatVector(max_out_length))
model.add(LSTM(units=hidden_size, return_sequences=True))
model.add(TimeDistributed(Dense(num_class, activation='softmax')))
For the input there is no problem as the network can accept different length inputs as long as the whole batch has the same length. However the problem is with the output size as its determined by the RepeatVector length and there is not easy way to change it.
Is there a solution for such a problem?