title: How to use tensorflow seq2seq without embeddings?
body: <p>I have been working on LSTM for timeseries forecasting by using tensorflow. Now, i want to try sequence to sequence (seq2seq). In the official site there is a tutorial which shows NMT with embeddings . So, how can I use this new seq2seq module without embeddings? (directly using time series "sequences").</p>
  
  <pre><code># 1. Encoder
  encoder_cell = tf.contrib.rnn.BasicLSTMCell(LSTM_SIZE)
  encoder_outputs, encoder_state = tf.nn.static_rnn(
    encoder_cell,
    x,
    dtype=tf.float32)
  
  # Decoder
  decoder_cell = tf.nn.rnn_cell.BasicLSTMCell(LSTM_SIZE)
  
  
  helper = tf.contrib.seq2seq.TrainingHelper(
      decoder_emb_inp, decoder_lengths, time_major=True)
  
  
  decoder = tf.contrib.seq2seq.BasicDecoder(
    decoder_cell, helper, encoder_state)
  
  # Dynamic decoding
  outputs, _ = tf.contrib.seq2seq.dynamic_decode(decoder)
  outputs = outputs[-1]
  
  # output is result of linear activation of last layer of RNN
  weight = tf.Variable(tf.random_normal([LSTM_SIZE, N_OUTPUTS]))
  bias = tf.Variable(tf.random_normal([N_OUTPUTS]))
  predictions = tf.matmul(outputs, weight) + bias
  </code></pre>
  
  <p>What should be the args for TrainingHelper() if I use input_seq=x and output_seq=label?</p>
  
  <p>decoder_emb_inp ???
  decoder_lengths ???</p>
  
  <p>Where input_seq are the first 8 point of the sequence, and output_seq are the last 2 point of the sequence.
  Thanks on advance!</p>